A method and apparatus for automatic disease state diagnosis

ABSTRACT

A method of automatically diagnosing pneumonia in a patient includes using an input/output interface device to obtain values of two or more diagnostic parameters of the patient from a carer for the patient. The method includes using a processor coupled to the input/output interface to apply the two or more diagnostic parameters to an electronic memory storing a plurality of precompiled pneumonia diagnostic models to identify an optimal diagnostic model for making a diagnosis. The values of the two or more diagnostic signs are applied to the identified optimal diagnostic model to generate a diagnosis output. The input/output interface device is operated in accordance with the diagnosis output to indicate the presence or absence of pneumonia in the patient to the carer. The carer may use the diagnosis to provide appropriate care to the patient. The pneumonia diagnostic models are derived from investigation of a population of pneumonia positive and non-pneumonia subjects.

TECHNICAL FIELD

The present invention concerns methods and apparatus for assistingmedical staff to diagnose and manage patients suffering from respiratorydysfunctions such as pneumonia.

RELATED APPLICATIONS

This application claims priority from Australian provisional patentapplications Nos. 2016903894 and 2016903896, both filed 26 Sep. 2016,the disclosures of which are hereby incorporated herein in theirentireties.

BACKGROUND ART

Any references to methods, apparatus or documents of the prior art arenot to be taken as constituting any evidence or admission that theyformed, or form part of the common general knowledge.

Pneumonia is one of the leading causes of mortality in children underfive worldwide. It is estimated that 905,059 children below the age offive died from pneumonia globally in 2013 [ref 1]. It accounted for 14%of a total of 6.3 million child deaths around the world that year [1,2]. The United Nations (UN) is aware of the issue and, through theMillenium Development Goal (MDG) 4 program, worked with countriesglobally to reduce the under-five mortality rate by two thirds in theperiod 1990 to 2015 [3-5].

Diagnostic tools used currently to diagnose pneumonia includestethoscopes and auscultation, chest X-ray, chest CT imaging, bloodanalysis, pulse oximetry, microbiology laboratory. There is no goldstandard to diagnose pneumonia. The definitive diagnosis of childhoodpneumonia, especially the early stage disease, is surprisingly difficulteven in a hospital. Lung aspiration biopsy may be the most effectiveapproach but it is clearly impractical for clinical use. The clinicalexamination and chest auscultation with a stethoscope are the firststeps in diagnosing childhood pneumonia. Auscultation requires a skilledphysician, and even then cannot provide a sensitive or specific enoughdiagnosis. Chest X-ray is often used as an important reference standardin confirming a clinical diagnosis. However, X-rays may not be sensitiveto early stage pneumonia or when the diseased part of the lung is notclearly visible on the image. In addition, normal X-ray may lead to poorspecificity of diagnosis in the presence of lung scarring or congestiveheart disease. X-ray CT imaging (Computed Tomography) and otherlaboratory analyses such as sputum tests, blood culture and C-reactiveprotein (CRP) tests may be needed to differentially diagnose pneumoniain some cases. None of the tests mentioned above can be used as a goldstandard. In hospitals in the developed world, the ‘reference standard’used is the clinical diagnosis aided with auscultation, radiology,laboratory and microbiology as needed. Often, the suspicion of pneumoniais enough to prescribe antibiotics. Even in a hospital, it is difficultto separate viral wheeze from viral pneumonia, for instance (viralpneumonia is the most common form of pneumonia).

Throughout the world, the differential diagnosis of pneumonia and otherrespiratory disease is a complicated problem. Other illnesses that needto be resolved are: bronchiolitis, asthma, viral wheeze, pneumonia,tracheobroncomalacia (TBM) and croup—all in children. Malaria andcongestive heart disease too can be symptomatically similar. In generaldiagnosing them is quite difficult even in a hospital setting. Long termmonitoring at a home/community setting is next to impossible.

In resource-poor areas of the world where pneumonia is rampant, it isalso difficult to find trained healthcare personnel with expertauscultation and clinical skills. The management of pneumonia in suchregions is largely dependent on community workers who visit remotecommunities.

In order to address these problems, the World Health Organization (WHO)has developed a simple clinical procedure to classify pneumonia inresource-limited regions. These classifications directly lead tointerventions such as antibiotic prescription and hospitalization. TheWHO procedure uses the symptom of cough (and/or breathing difficulty) asthe screening-in feature for pneumonia; breathing rate then determinesif pneumonia exists. The disease will be further classified as severepneumonia if symptoms such as chest recession are also present.

Even though the breathing rate based WHO procedures perform poorly, inremote areas where over 90% of childhood pneumonia occur, it is the mainpneumonia classification methods used to determine treatment/referraldecisions.

In the past, several studies have explored the performance of the WHOprocedure and its variants in pneumonia diagnosis. They reported areasonably high sensitivity (69-94%) but an unacceptably poorspecificity (67-16%). Researchers have attempted to improve thespecificity of the WHO criteria using different approaches. Theseinclude the augmentation of WHO procedure by considering fever and othersymptoms of pneumonia (nasal flaring, poor sleep, chest in-drawing,cough lasting longer than two days etc.). These efforts resulted in asensitivity and specificity within the range 20-90%, but higherspecificities were achieved only at the cost of lower sensitivity andvice versa. They also suffer from the fact that the higher thecomplexity of measurements, the more difficult it is to train communityworkers to reliably implement the procedure in field visits.

The WHO procedure uses breathing rate as the key measurement. However,obtaining the breathing rate is notoriously difficult in children. Avast amount of resources have been committed by agencies such as theBill & Melinda Gates Foundation, Unicef and WHO to develop breathingrate counters. Seehttps://www.path.org/publications/files/TS_update_rr_counters.pdf for anexample. In the manual diagnosis of pneumonia, different clinical signsand measurements are dichotomized for the ease of assessment by aclinician. For instance, breathing rates above age indexed thresholdsare used to declare the existence of pneumonia. Clinicians may also notethe existence/absence of cough, fever, chest-in-drawing etc.

An issue with using only cough sounds is the number of coughs required(5-10 cough events) for reliable analysis. It is known that infantpatients may not readily cough when required in order to use it foranalysis. Furthermore, as the patient's condition gets worse, it is alsoknown that cough symptoms may vanish due to their having a weakenedbody.

It will therefore be realised that one of the key developments stillmissing in the global fight against pneumonia is the absence of a rapid,low cost diagnostic method/system[1, 8-14]. Diagnosing each caseaccurately and precisely is difficult even with state of the artequipment, and even more so in poor resource settings.

It is an object of the present invention to provide a method andapparatus for assisting in the diagnosis of a disease state such aspneumonia which is straightforward for a clinician to use and whichaddresses one or more of the above described problems of the prior art.

SUMMARY OF THE INVENTION

According to a first aspect of the present invention there is provided amethod for automatically providing a carer of a patient with a diseasestate diagnosis of the patient including the steps of:

-   -   providing a diagnostic application software product including a        multiplicity of diagnostic models derived from investigation of        a population containing disease state positive and non-disease        state subjects;    -   operating an input/output interface of an electronic device        including a memory storing the software product to prompt the        carer for identification of a number of disease diagnostic        parameters and values therefor for the patient;    -   operating a processor of the electronic device to select one of        the diagnostic models from the memory based on the identified        disease diagnostic parameters;    -   operating the processor to apply the values of the disease        diagnostic parameters to the selected diagnostic model; and    -   presenting a diagnosis to the carer on the input/output        interface based upon results of the application of the        parameters to the selected diagnostic model for the carer to use        in providing therapy to the patient.

Preferably said diagnostic parameters comprise: breathing rate,existence of fever, existence of runny nose, number of days with runnynose, number of days with cough, existence of chest indrawing,temperature, BMI (body mass index), and oxygen saturation level;

In an embodiment of the invention the method includes selecting one ofthe diagnostic models with reference to one or more look up tablescorrelating diagnostic performance of models against availablediagnostic parameters.

The method may include prompting for user choice of a diagnostic modeloptimized for “sensitivity”, “specificity” or “accuracy” wherein themethod includes operating the electronic device to select one of thediagnostic models taking into account the optimization choice.

In an embodiment the method includes operating the device to determineif the values of diagnostic parameters indicate patient danger signs.

The method may include checking if the diagnostic values for the patientindicate that the patient is presenting general danger signs accordingto World Health Guidelines.

The method may include saving diagnostic results to a remote serverwhereby diagnostic results may be saved and compared from a plurality ofdiagnostic devices.

In some embodiments of the invention the method includes prompting forrecording of at least one patient cough sound.

The method may include requiring the recording of no more than twopatient cough sounds.

Preferably the method includes applying the at least one patient coughsound to a cough feature extraction engine of the diagnostic applicationto generate cough features.

The patient cough features may be applied to the diagnostic model toassist the diagnosis.

In a preferred embodiment of the invention the diagnostic parameterscomprise breathing rate, temperature, heart rate and cough soundanalysis.

According to another aspect of the present invention there is provided adiagnostic device arranged to prompt a clinician to input diagnosticinformation for a patient and further arranged to automatically presenta diagnosis to the clinician, the diagnostic device including:

-   -   an electronic memory storing instructions comprising a        diagnostic application software product including a plurality of        diagnostic models derived from investigation of        pneumonia-positive and non-pneumonia subjects;    -   an electronic processor in communication with the electronic        memory for executing the instructions;    -   a user interface responsive to the electronic processor for the        processor to prompt for and receive the diagnostic information;    -   wherein the processor is configured by the diagnostic        application in use to present a diagnosis of the patient with        the user interface by applying the diagnostic information to at        least one of said diagnostic models.

Preferably the diagnostic device includes a microphone and audiointerface coupling the microphone to the electronic processor.

The diagnostic application may include a cough feature extraction engineand whereby in use the processor applies the cough sound to the coughfeature extraction engine to produce cough features thereof.

In an embodiment of the invention the diagnostic device is configured bythe diagnostic application in use to apply the cough features to the atleast one of said diagnostic models.

According to a further aspect of the present invention there is provideda method of automatically diagnosing pneumonia in a patient comprising:

-   -   using an input/output interface device to obtain values of two        or more diagnostic parameters of the patient from a carer for        the patient;    -   said diagnostic parameters comprising: breathing rate, existence        of fever, existence of runny nose, number of days with runny        nose, number of days with cough, existence of chest indrawing,        temperature, BMI (body mass index), and oxygen saturation level;    -   using a processor device operatively coupled to the input/output        interface to apply the two or more diagnostic parameters to an        electronic memory storing a plurality of precompiled pneumonia        diagnostic models to thereby identify an optimal diagnostic        model of said plurality;    -   applying the values of the two or more diagnostic signs to the        identified optimal diagnostic model to generate a diagnosis        output from the diagnostic model; and    -   operating the input/output interface device in accordance with        the diagnosis output to indicate presence or absence of        pneumonia in the patient to the carer, for use by the carer in        providing care to the patient;    -   wherein the pneumonia diagnostic models are derived from        investigation of a population of pneumonia positive patients and        non-pneumonia positive subjects.

According to another aspect of the present invention there is provided amethod for operating an electronic processor based electronic device todiagnose a disease state of a patient including the steps of:

-   -   installing a diagnostic application software product on the        electronic device, the diagnostic application including a        multiplicity of diagnostic models;    -   prompting for identification of a number of disease diagnostic        parameters and values therefor for the patient;    -   operating the electronic device to select one of the diagnostic        models based on the identified disease diagnostic parameters;    -   operating the electronic device to apply the values of the        disease diagnostic parameters to the selected diagnostic model;        and    -   presenting a diagnosis to the clinician based upon results of        the application of the parameters to the selected diagnostic        model.

BRIEF DESCRIPTION OF THE DRAWINGS

Preferred features, embodiments and variations of the invention may bediscerned from the following Detailed Description which providessufficient information for those skilled in the art to perform theinvention. The Detailed Description is not to be regarded as limitingthe scope of the preceding Summary of the Invention in any way. TheDetailed Description will make reference to a number of drawings asfollows:

FIG. 1 is a block diagram of a diagnostic device according to anembodiment of the present invention shown in use.

FIG. 2 is a flowchart of a method performed by the diagnostic device ofFIG. 1 in accordance an embodiment of the invention.

FIGS. 3 to 5 depict screens generated by the diagnostic device fordetecting WHO general danger signs of a patient.

FIG. 6 depicts a screen generated by the diagnostic device for aclinician to enter diagnostic information for a patient.

FIG. 7 depicts a screen generated by the diagnostic device to confirmthe entered diagnostic information to the clinician.

FIG. 8 is a flowchart illustrating a diagnostic model selectionprocedure by the diagnostic device at box 43 of the flowchart of FIG. 2.

FIG. 9 is a flowchart illustrating a procedure of the diagnostic deviceduring selection of the best diagnostic model.

FIG. 10 depicts a screen generated by the diagnostic device whilst it isanalyzing the diagnostic information.

FIG. 11 depicts a screen generated by the diagnostic device presentingthe results of the analysis to the clinician.

FIG. 11A is a block diagram of a diagnostic device according to afurther embodiment of the present invention which is arranged to takeinto account cough sounds of the patient.

FIGS. 11A to 11D are screens generated by the diagnostic device of FIG.11A for prompting for cough sounds of the patient and for analyzing andpresenting diagnostic results for the patient.

FIG. 12 is a flow diagram detailing the process used to analyse coughsound data for designing a diagnostic model for use in an embodiment ofthe present invention.

FIG. 13 is a flow diagram illustrating a method for training adiagnostic model for use in an embodiment of the present invention.

FIG. 14 comprises graphs representing mean Sn (sensitivity) and Sp(specificity) values from training models calculated from the age group2-11 month. Frames from top to bottom are arranged by the number offeatures used to create the model: one (top), two (middle), and threefeatures (bottom) taken at a time. n is the number of possible featurecombinations using one, two, three features at a time. Significantimprovements in specificity can be seen as more features were used.

FIG. 15 comprises ROC curve analysis for the age group of 2-11 month.The solid line in each frame represents the mean ROC curve, formed overk-iterations. Crosses on each frame represent the SD of the Sn and 1-Spat points shown. On each frame, the performance of WHO/IMCI procedurefor resource-limited regions is graphically illustrated (see boxes). Thecenter of the box indicates the mean performance, and the height andwidth of the box represent SD.

FIG. 16 comprises ROC curve analysis for the age group of 12-60 month.The solid line in each frame represents the mean ROC curve, formed overk-iterations. Crosses on each frame represent the SD of the Sn and 1-Spat points shown. On each frame, the performance of WHO/IMCI procedurefor resource-limited regions is graphically illustrated (see boxes). Thecenter of the box indicates the mean performance, and the height andwidth of the box represent SD.

FIG. 17 is a graph showing the training and testing performance of thetwo best LRM models for 2-11 months and 12-60 months age group. The bestLRM models for 2-11 months group were models using runny nose/days withrunny nose/breathing rate/temperature and runny nose/days with runnynose/breathing rate/heart rate features. As for the 12-60 months group,the best models were based on fever/days with cough/heart rate/chestindrawing and runny nose/days with runny nose/breathing rate/temperaturefeature combinations.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

Referring initially to FIG. 1 there is shown a diagnostic device 1according to an embodiment of the present invention. The diagnosticdevice 1 comprises a unique combination of hardware and softwarespecifically designed to assist a clinician to diagnose, and so be ableto treat, a disease state such as pneumonia. In the embodiment of theinvention that is illustrated in FIG. 1 the device 1 includes one ormore processors in the form of microprocessor 3 and an electronic memory5 that is accessible to the microprocessor 3 and which storesinstructions that are executable by the microprocessor 3 in order forthe diagnostic device 1 to process diagnostic signs of a patient 2 andpresent a diagnosis to carer in the form of clinician 4. The Diagnosticapplication 6 includes a plurality of diagnostic models 20 which arestored in memory 5 and a model lookup table 22 which the microprocessor3 uses to determine the most appropriate diagnostic model based onavailable diagnostic parameters for a patient 2. As is explained inAppendix B of this specification, with reference to Table 9, theInventors drew on their quantitative research of over 131,000combinations of features associated with pneumonia to arrive at thediagnostic models that are used.

The electronic memory 5 includes an operating system 8 such as theAndroid operating system or the Apple iOS operating system, for example,for execution by the microprocessor 3. The electronic memory 5 alsoincludes a diagnostic application software product 6 according to apreferred embodiment of the present invention.

The microprocessor 3 is in data communication with a plurality ofperipheral assemblies 9 to 23, as indicated in FIG. 1, via a data bus 7.The data bus consists of metal tracks which convey electrical datasignals 13 between the various assemblies of the device 1. Thediagnostic device 1 is able to establish voice and data communicationwith a voice and/or data communications network 31 via WAN/WLAN assembly23 and radio frequency antenna 29.

Although the diagnostic device 1 that is illustrated in FIG. 1 ismicroprocessor based it will be realised that it could also beimplemented using other types of technology. For example it could beimplemented using a Field Programmable Gate Array (FPGA) or discretelogic circuits.

The diagnostic device 1 requires no external physiological sensors,physical contact with patient 2 or access to communication network 31 tooperate. As will be explained the method is automated andstraightforward to use. In some embodiments the device 1 collectspatient metadata, diagnosis and treatment/referral information (e.g.whether or not an antibiotic was dispensed) along with GPS coordinatesat the site of the diagnosis. The device logs the information or willtransfer it over a WAN data network, 31, e.g. the Internet, to a cloudserver 33 when/if network 31 is available. In one embodiment the devicemakes it possible to track pandemics. It can also take into accountdeveloping epidemics in diagnosing patients by accessing geographicalinformation stored in the cloud server 33 of the diagnosis of disease inother locations using similar devices.

Referring now to FIG. 2, there is shown a flowchart of a methodaccording to a preferred embodiment of the present invention, which thediagnostic device implements under the control of the instructions thatare coded into the diagnostic application 6.

At box 35 of FIG. 2, the microprocessor 3 operates an input/outputinterface in the form of LCD touch screen interface 11 to display aprompt for a user, e.g. clinician 4, to enter diagnostic parameters ofpatient 2. FIG. 3 shows the diagnostic device 1 displaying a data entryscreen 57 on LCD touch screen interface 11 for the clinician 4 to enterthe patient parameters.

The diagnostic program 6 includes instructions based on the Inventors'finding that there are at least 17 patient parameters that may be of usein diagnosing a disease state, such as pneumonia as shown in Table 1Abelow:

TABLE 1A Seventeen Diagnostic Parameters for Pneumonia Age Months WeightKg Height m Breathing rate Breaths/min Heart rate Beats/min Temperature° C. BMI Number Oxygen saturation Percentage Chest indrawing Yes/NoFever Yes/No Days with fever Days Cough Yes/No Days with cough DaysRunny nose Yes/No Days with runny nose Days Breathing difficulty Yes/NoDays with breathing difficulty Days

The Inventors have discovered that the most straightforward patientparameters to obtain and which are effective in determining the presenceof a respiratory disease state such as pneumonia are the nine diagnosticparameters that are set out in table 1B below.

TABLE 1B Subset of Nine Important Patient Diagnostic Sign ParametersCode Diagnostic Sign A Breathing rate B Fever C Existence of runny noseD Days with runny nose E Days with cough F Chest indrawing G TemperatureH BMI I Oxygen saturation — Not used

A preferred embodiment of diagnostic device 1 is configured to operatein the World Health Organization (WHO) framework and this can be seen inthe way that the screen 57 provides for the clinician 4 to select WHOScreening Criteria, namely “Cough” and “Difficulty Breathing” and alsoto enter WHO General Danger Signs as listed in screen 57. At box 37 ofthe flowchart of FIG. 2, if the clinician 4 has indicated on screen 57that the patient 2 is displaying WHO general danger signs thenmicroprocessor control diverts to box 51 at which point themicroprocessor 3 operates screen 59 (FIG. 4) to alert the clinician 4that the patient 2 is indicating severe pneumonia or disease accordingto the WHO general danger signs criteria. Alternatively, if at box 37the patients' parameters do not indicate the WHO general danger signsthen control diverts to box 39. At box 39 the microprocessor checkswhether the patient parameters indicate that the WHO criteria forfurther screening have been met. If they have not been met then controldiverts to box 53 at which point the microprocessor 5 causes the screen61 that is shown in FIG. 5 to display thereby indicating to theclinician 4 that the patient 2 exhibits neither the WHO general dangersigns nor the WHO screening criteria.

Alternatively, if at box 39 the microprocessor 5, executing a furtherinstruction of diagnostic application 6, determines that the patient'sparameters indicate that the WHO screening criteria parameters are metthen control diverts to box 41.

At box 41 the microprocessor 5 causes screen 63 (FIG. 6) to display onLCD touchscreen 11 to prompt for patient diagnostic parameters from theclinician 4.

Screen 63 presents selection buttons 65A, . . . ,65I for the ninepatient parameters that have previously been referred to in Table 1 B.

In the presently described embodiment the clinician 4 is required toselect at least three of the diagnostic parameters of Table 1B by meansof the selection buttons 65A . . . ,65I. Control then diverts to box 43(FIG. 2) wherein microprocessor 3 displays screen 67 (FIG. 7) oninterface 11. In the presently described preferred embodiment of theinvention screen 67 includes selection buttons 69, 71 and 73 forclinician 4 to indicate a preference for the diagnostic model that willbe used to optimize for any one of diagnostic “sensitivity”,“specificity” or “accuracy”. These terms have the following meanings:

Sensitivity: (also called the true positive rate, the recall, orprobability of detection in some fields) measures the proportion ofpositives that are correctly identified as such (e.g., the percentage ofsick people who are correctly identified as having the condition).

Specificity: (also called the true negative rate) measures theproportion of negatives that are correctly identified as such (e.g., thepercentage of healthy people who are correctly identified as not havingthe condition).

Accuracy: (ACC)=(ΣTrue positive+ΣTrue negative)/ΣTotal population

At box 43, the microprocessor 3 selects the optimal diagnostic model,amongst models 20 which are stored in memory 5, based on the patientparameters that have been submitted. To select the best of models 20 forthe information that the clinician has entered, the microprocessor 3, asconfigured by diagnostic program 6, queries diagnostic model lookuptables 22 which are stored in memory 5 and which the Inventors haveproduced. (It will be realised that in other embodiments of theinvention, wherein a high power processor is used, it may be possible togenerate the required information as required without resort to look uptables.)

The content of the diagnostic lookup tables 22 is set out as follows:

Table 2A—Patient 2 to 11 Months of Age—Diagnostic Model OrderedAccording to Descending Sensitivity. Table 2B—Patient 2 to 11 Months ofAge—Diagnostic Model Ordered According to Descending Specificity Table2C—Patient 2 to 11 Months of Age—Diagnostic Model Ordered According toDescending Specificity. Table 3A—Patient 12 to 60 Months ofAge—Diagnostic Model Ordered According to Descending Sensitivity Table3B—Patient 12 to 60 Months of Age—Diagnostic Model Ordered According toDescending Specificity. Table 3C—Patient 12 to 60 Months ofAge—Diagnostic Model Ordered According to Descending Accuracy.

The patient diagnostic sign parameters that make up each combination inthe combination columns of the following Tables 2A to C have previouslybeen defined in Table 1 B.

TABLE 2A Patient 2 to 11 Months of Age - Diagnostic Model OrderedAccording to Descending Sensitivity. Ordered according to descendingsensitivity SEN SPE ACC PPV NPV No Combination (%) (%) (%) (%) (%) 1CEF- 96 50 77 74 81 2 EFI- 94 54 77 76 85 3 CDF- 92 52 74 74 73 4 CDEF92 56 76 74 86 5 ACDI 91 70 82 82 89 6 ACFG 91 50 73 73 77 7 ABCD 91 6177 76 88 8 ABI- 91 51 73 72 85 9 ABCI 91 51 73 72 85 10 BFI- 91 58 78 7586 11 BCFI 91 58 78 75 86 12 BFGI 91 58 78 75 86 13 ACDH 89 64 77 78 7414 CEFI 89 57 77 76 81 15 DEFI 89 55 76 74 81 16 ACDG 89 53 73 73 72 17AFH- 89 50 72 72 71 18 ADI- 89 54 75 74 83 19 ADEI 89 55 75 73 83 20AEF- 89 54 73 72 77 21 AEFH 89 54 73 73 73 22 BEFI 88 64 79 80 79 23BEF- 88 66 79 79 84 24 BCEF 88 66 79 79 84 25 BFHI 88 62 78 77 83 26BFH- 88 62 78 76 82 27 BFGH 88 62 78 76 82 28 BEFG 88 59 76 75 82 29ABFI 88 56 75 73 81 30 BCFH 88 54 75 73 80 31 ABEI 88 51 72 71 81 32ACD- 88 66 76 77 78 33 EFGI 88 53 74 74 77 34 ACDE 88 60 73 75 78 35ACDF 87 53 71 73 71 36 EFHI 87 64 79 79 82 37 BCDF 87 51 72 72 77 38AFHI 87 50 72 72 77 39 CDHI 86 54 76 74 75 40 BDEF 86 66 78 79 82 41BEFH 86 64 76 77 81 42 ABF- 86 56 74 72 80 43 ABCF 86 56 74 72 80 44ADHI 86 51 72 72 79 45 ABC- 86 53 70 70 78 46 AB-- 86 50 69 69 78 47FGHI 86 54 74 73 80 48 ADFI 85 58 75 74 82 49 DFHI 85 53 74 73 0 50 ABFH84 59 74 73 78 51 ABEF 84 59 73 73 79 52 ABE- 84 57 71 71 78 53 ABCE 8457 71 71 78 54 ABH- 84 53 69 69 76 55 ABCH 84 53 69 69 76 56 ABFG 84 5270 70 76 57 ABG- 84 54 69 70 77 58 ABEG 81 57 69 70 76 59 ABEH 81 57 6970 76 60 ABCG 81 52 66 69 74 61 DEHI 77 57 73 73 67

TABLE 2B Patient 2 to 11 Months of Age - Diagnostic Mode OrderedAccording to Descending Specificity Ordered according to descendingspecificity SEN SPE ACC PPV NPV No Combination (%) (%) (%) (%) (%) 1ACDI 91 70 82 82 89 2 BEF- 88 66 79 79 84 3 BCEF 88 66 79 79 84 4 BDEF86 66 78 79 82 5 ACD- 88 66 76 77 78 6 BEFI 88 64 79 80 79 7 EFHI 87 6479 79 82 8 ACDH 89 64 77 78 74 9 BEFH 86 64 76 77 81 10 BFHI 88 62 78 7783 11 BFH- 88 62 78 76 82 12 BFGH 88 62 78 76 82 13 ABCD 91 61 77 76 8814 ACDE 88 60 73 75 78 15 BEFG 88 59 76 75 82 16 ABFH 84 59 74 73 78 17ABEF 84 59 73 73 79 18 BFI- 91 58 78 75 86 19 BCFI 91 58 78 75 86 20BFGI 91 58 78 75 86 21 ADFI 85 58 75 74 82 22 CEFI 89 57 77 76 81 23DEHI 77 57 73 73 67 24 ABE- 84 57 71 71 78 25 ABCE 84 57 71 71 78 26ABEG 81 57 69 70 76 27 ABEH 81 57 69 70 76 28 ABFI 88 56 75 73 81 29ABF- 86 56 74 72 80 30 ABCF 86 56 74 72 80 31 CDEF 92 56 76 74 86 32DEFI 89 55 76 74 81 33 ADEI 89 55 75 73 83 34 ABG- 84 54 69 70 77 35AEF- 89 54 73 72 77 36 AEFH 89 54 73 73 73 37 EFI- 94 54 77 76 85 38ADI- 89 54 75 74 83 39 CDHI 86 54 76 74 75 40 BCFH 88 54 75 73 80 41FGHI 86 54 74 73 80 42 EFGI 88 53 74 74 77 43 DFHI 85 53 74 73 0 44 ACDG89 53 73 73 72 45 ACDF 87 53 71 73 71 46 ABC- 86 53 70 70 78 47 ABH- 8453 69 69 76 48 ABCH 84 53 69 69 76 49 ABFG 84 52 70 70 76 50 ABCG 81 5266 69 74 51 CDF- 92 52 74 74 73 52 ADHI 86 51 72 72 79 53 BCDF 87 51 7272 77 54 ABI- 91 51 73 72 85 55 ABCI 91 51 73 72 85 56 ABEI 88 51 72 7181 57 CEF- 96 50 77 74 81 58 AFHI 87 50 72 72 77 59 AB-- 86 50 69 69 7860 ACFG 91 50 73 73 77 61 AFH- 89 50 72 72 71

TABLE 2C Patient 2 to 11 Months of Age - Diagnostic Model OrderedAccording to Descending Specificity. Ordered according to descendingaccuracy SEN SPE ACC PPV NPV No Combination (%) (%) (%) (%) (%) 1 ACDI91 70 82 82 89 2 BEF- 88 66 79 79 84 3 BCEF 88 66 79 79 84 4 EFHI 87 6479 79 82 5 BEFI 88 64 79 80 79 6 BFHI 88 62 78 77 83 7 BFI- 91 58 78 7586 8 BCFI 91 58 78 75 86 9 BFGI 91 58 78 75 86 10 BDEF 86 66 78 79 82 11BFH- 88 62 78 76 82 12 BFGH 88 62 78 76 82 13 CEF- 96 50 77 74 81 14ABCD 91 61 77 76 88 15 ACDH 89 64 77 78 74 16 EFI- 94 54 77 76 85 17CEFI 89 57 77 76 81 18 BEFH 86 64 76 77 81 19 BEFG 88 59 76 75 82 20CDEF 92 56 76 74 86 21 ACD- 88 66 76 77 78 22 CDHI 86 54 76 74 75 23DEFI 89 55 76 74 81 24 ADI- 89 54 75 74 83 25 ADFI 85 58 75 74 82 26ABFI 88 56 75 73 81 27 ADEI 89 55 75 73 83 28 BCFH 88 54 75 73 80 29FGHI 86 54 74 73 80 30 DFHI 85 53 74 73 0 31 CDF- 92 52 74 74 73 32 EFGI88 53 74 74 77 33 ABFH 84 59 74 73 78 34 ABF- 86 56 74 72 80 35 ABCF 8656 74 72 80 36 ABEF 84 59 73 73 79 37 ABI- 91 51 73 72 85 38 ABCI 91 5173 72 85 39 ACFG 91 50 73 73 77 40 AEF- 89 54 73 72 77 41 ACDE 88 60 7375 78 42 ACDG 89 53 73 73 72 43 DEHI 77 57 73 73 67 44 AEFH 89 54 73 7373 45 ADHI 86 51 72 72 79 46 BCDF 87 51 72 72 77 47 AFHI 87 50 72 72 7748 AFH- 89 50 72 72 71 49 ABEI 88 51 72 71 81 50 ACDF 87 53 71 73 71 51ABE- 84 57 71 71 78 52 ABCE 84 57 71 71 78 53 ABC- 86 53 70 70 78 54ABFG 84 52 70 70 76 55 ABEG 81 57 69 70 76 56 ABEH 81 57 69 70 76 57ABH- 84 53 69 69 76 58 ABCH 84 53 69 69 76 59 AB-- 86 50 69 69 78 60ABG- 84 54 69 70 77 61 ABCG 81 52 66 69 74

TABLE 3A Patient 12 to 60 Months of Age - Diagnostic Model OrderedAccording to Descending Sensitivity. Ordered according to descendingsensitivity SEN SPE ACC PPV NPV No Combination (%) (%) (%) (%) (%) 1BEF- 94 51 81 81 67 2 BFI- 92 51 79 80 71 3 BEFI 92 51 79 80 65 4 CDF-92 55 81 82 65 5 CDFH 92 50 78 80 65 6 BCEF 91 51 79 81 64 7 BDEF 91 5179 81 64 8 AD-- 91 60 74 76 71 9 ADH- 91 60 74 76 71 10 AEF- 90 51 74 7571 11 ACEF 90 51 74 75 71 12 CDEI 89 55 80 80 66 13 CDFI 89 53 79 80 6314 CDFG 89 55 79 82 63 15 ADI- 89 62 74 78 71 16 ABCE 89 54 74 77 67 17ABEG 89 53 75 77 67 18 ABEH 89 53 75 77 56 19 ADGI 89 62 74 78 71 20ADHI 89 62 74 78 67 21 DFI- 89 54 76 76 67 22 ADFI 89 54 76 76 73 23CEFI 89 50 79 79 59 24 DEFI 89 50 79 79 57 25 ACD- 89 66 78 81 66 26BEFH 89 51 78 81 62 27 ACDH 89 72 79 82 70 28 AFHI 89 51 73 76 69 29ABC- 88 53 71 77 60 30 ABF- 88 58 74 79 73 31 ABCD 88 63 77 82 74 32BCDF 88 51 76 80 67 33 ABCG 88 55 73 78 60 34 ABCH 88 53 71 77 60 35ABCF 88 61 76 80 73 36 ABFG 88 58 74 79 73 37 AH-- 88 58 73 75 77 38AF-- 88 51 73 75 65 39 ADG- 88 56 71 74 69 40 AFH- 88 64 74 77 77 41ADGH 88 62 74 77 71 42 ADFG 88 50 73 74 69 43 ADFH 88 51 73 75 71 44BFGI 88 60 78 79 71 45 ABI- 88 75 77 83 76 46 CFHI 88 50 73 75 65 47AI-- 88 56 70 75 70 48 ACI- 88 54 70 74 74 49 AGI- 88 51 68 74 57 50AFI- 88 64 73 78 76 51 ABFI 88 80 81 85 78 52 ABFH 88 51 71 76 74 53ACDI 88 74 78 82 76 54 ACGI 88 51 68 74 57 55 ACFI 88 64 73 78 76 56AFGI 88 64 73 78 76 57 AEFH 88 51 73 74 69 58 CFGI 87 58 76 77 69 59FGHI 87 52 73 76 58 60 ABEI 87 60 76 79 63 61 AGHI 87 56 70 75 65 62EFI- 86 58 81 83 55 63 EFGI 86 58 81 83 55 64 ACH- 86 58 71 75 71 65ACF- 86 51 71 75 60 66 ACFH 86 64 73 77 73 67 ABCI 86 71 74 80 74 68AEFG 86 52 73 76 63 69 ABDI 86 71 74 81 70 70 ABGI 86 66 73 79 70 71ABGH 86 53 69 77 57 72 DFGI 85 57 75 77 64 73 AHI- 85 56 68 75 64 74AEFI 85 61 75 80 61 75 ACHI 85 56 68 75 64 76 ACDE 84 60 75 81 52 77EFHI 84 51 77 80 52 78 ACDG 84 72 76 83 65 79 ACDF 84 51 75 80 55 80BFHI 84 60 75 79 67 81 AG-- 84 60 70 75 69 82 ACG- 84 64 71 77 71 83AGH- 84 64 71 77 71 84 AFG- 84 66 73 78 71 85 ACGH 84 66 73 78 71 86ACFG 84 64 71 77 71 87 AFGH 84 64 71 77 71 88 ABHI 83 60 68 76 64 89CDEF 82 51 73 76 56

TABLE 3B Patient 12 to 60 Months of Age - Diagnostic Model OrderedAccording to Descending Specificity. Ordered according to descendingspecificity SEN SPE ACC PPV NPV No Combination (%) (%) (%) (%) (%) 1ABFI 88 80 81 85 78 2 ABI- 88 75 77 83 76 3 ACDI 88 74 78 82 76 4 ACDH89 72 79 82 70 5 ACDG 84 72 76 83 65 6 ABCI 86 71 74 80 74 7 ABDI 86 7174 81 70 8 ABGI 86 66 73 79 70 9 AFG- 84 66 73 78 71 10 ACGH 84 66 73 7871 11 ACD- 89 66 78 81 66 12 AFH- 88 64 74 77 77 13 AFI- 88 64 73 78 7614 ACFI 88 64 73 78 76 15 AFGI 88 64 73 78 76 16 ACFH 86 64 73 77 73 17ACG- 84 64 71 77 71 18 AGH- 84 64 71 77 71 19 ACFG 84 64 71 77 71 20AFGH 84 64 71 77 71 21 ABCD 88 63 77 82 74 22 ADI- 89 62 74 78 71 23ADGI 89 62 74 78 71 24 ADHI 89 62 74 78 67 25 ADGH 88 62 74 77 71 26AEFI 85 61 75 80 61 27 ABCF 88 61 76 80 73 28 AD-- 91 60 74 76 71 29ADH- 91 60 74 76 71 30 AG-- 84 60 70 75 69 31 ABHI 83 60 68 76 64 32BFGI 88 60 78 79 71 33 ABEI 87 60 76 79 63 34 ACDE 84 60 75 81 52 35BFHI 84 60 75 79 67 36 ABF- 88 58 74 79 73 37 ABFG 88 58 74 79 73 38CFGI 87 58 76 77 69 39 AH-- 88 58 73 75 77 40 ACH- 86 58 71 75 71 41EFI- 86 58 81 83 55 42 EFGI 86 58 81 83 55 43 DFGI 85 57 75 77 64 44ADG- 88 56 71 74 69 45 AI-- 88 56 70 75 70 46 AGHI 87 56 70 75 65 47AHI- 85 56 68 75 64 48 ACHI 85 56 68 75 64 49 CDF- 92 55 81 82 65 50CDEI 89 55 80 80 66 51 CDFG 89 55 79 82 63 52 ABCG 88 55 73 78 60 53ABCE 89 54 74 77 67 54 DFI- 89 54 76 76 67 55 ADFI 89 54 76 76 73 56ACI- 88 54 70 74 74 57 CDFI 89 53 79 80 63 58 ABEG 89 53 75 77 67 59ABEH 89 53 75 77 56 60 ABC- 88 53 71 77 60 61 ABCH 88 53 71 77 60 62ABGH 86 53 69 77 57 63 AEFG 86 52 73 76 63 64 FGHI 87 52 73 76 58 65AEF- 90 51 74 75 71 66 ACEF 90 51 74 75 71 67 AFHI 89 51 73 76 69 68AF-- 88 51 73 75 65 69 ADFH 88 51 73 75 71 70 AGI- 88 51 68 74 57 71ABFH 88 51 71 76 74 72 ACGI 88 51 68 74 57 73 AEFH 88 51 73 74 69 74ACF- 86 51 71 75 60 75 BEF- 94 51 81 81 67 76 BFI- 92 51 79 80 71 77BEFI 92 51 79 80 65 78 BCEF 91 51 79 81 64 79 BDEF 91 51 79 81 64 80BEFH 89 51 78 81 62 81 BCDF 88 51 76 80 67 82 ACDF 84 51 75 80 55 83CDEF 82 51 73 76 56 84 EFHI 84 51 77 80 52 85 CDFH 92 50 78 80 65 86CFHI 88 50 73 75 65 87 CEFI 89 50 79 79 59 88 DEFI 89 50 79 79 57 89ADFG 88 50 73 74 69

TABLE 3C Patient 12 to 60 Months of Age - Diagnostic Model OrderedAccording to Descending Accuracy. Ordered according to descendingaccuracy SEN SPE ACC PPV NPV No Combination (%) (%) (%) (%) (%) 1 EFI-86 58 81 83 55 2 EFGI 86 58 81 83 55 3 BEF- 94 51 81 81 67 4 CDF- 92 5581 82 65 5 ABFI 88 80 81 85 78 6 CDEI 89 55 80 80 66 7 CEFI 89 50 79 7959 8 DEFI 89 50 79 79 57 9 BFI- 92 51 79 80 71 10 BEFI 92 51 79 80 65 11BCEF 91 51 79 81 64 12 BDEF 91 51 79 81 64 13 CDFI 89 53 79 80 63 14CDFG 89 55 79 82 63 15 ACDH 89 72 79 82 70 16 CDFH 92 50 78 80 65 17ACD- 89 66 78 81 66 18 BEFH 89 51 78 81 62 19 BFGI 88 60 78 79 71 20ACDI 88 74 78 82 76 21 ABCD 88 63 77 82 74 22 ABI- 88 75 77 83 76 23EFHI 84 51 77 80 52 24 BCDF 88 51 76 80 67 25 ABEI 87 60 76 79 63 26ACDG 84 72 76 83 65 27 DFI- 89 54 76 76 67 28 ADFI 89 54 76 76 73 29ABCF 88 61 76 80 73 30 CFGI 87 58 76 77 69 31 ABEG 89 53 75 77 67 32ABEH 89 53 75 77 56 33 DFGI 85 57 75 77 64 34 AEFI 85 61 75 80 61 35ACDE 84 60 75 81 52 36 ACDF 84 51 75 80 55 37 BFHI 84 60 75 79 67 38AD-- 91 60 74 76 71 39 ADH- 91 60 74 76 71 40 AEF- 90 51 74 75 71 41ACEF 90 51 74 75 71 42 ADI- 89 62 74 78 71 43 ABCE 89 54 74 77 67 44ADGI 89 62 74 78 71 45 ADHI 89 62 74 78 67 46 ABF- 88 58 74 79 73 47ABFG 88 58 74 79 73 48 AFH- 88 64 74 77 77 49 ADGH 88 62 74 77 71 50ABCI 86 71 74 80 74 51 ABDI 86 71 74 81 70 52 CFHI 88 50 73 75 65 53FGHI 87 52 73 76 58 54 CDEF 82 51 73 76 56 55 AFHI 89 51 73 76 69 56AH-- 88 58 73 75 77 57 AF-- 88 51 73 75 65 58 ADFG 88 50 73 74 69 59ADFH 88 51 73 75 71 60 AFI- 88 64 73 78 76 61 ACFI 88 64 73 78 76 62AFGI 88 64 73 78 76 63 AEFH 88 51 73 74 69 64 ACFH 86 64 73 77 73 65AEFG 86 52 73 76 63 66 ABGI 86 66 73 79 70 67 AFG- 84 66 73 78 71 68ACGH 84 66 73 78 71 69 ABCG 88 55 73 78 60 70 ADG- 88 56 71 74 69 71ABFH 88 51 71 76 74 72 ACH- 86 58 71 75 71 73 ACF- 86 51 71 75 60 74ACG- 84 64 71 77 71 75 AGH- 84 64 71 77 71 76 ACFG 84 64 71 77 71 77AFGH 84 64 71 77 71 78 ABC- 88 53 71 77 60 79 ABCH 88 53 71 77 60 80AI-- 88 56 70 75 70 81 ACI- 88 54 70 74 74 82 AGHI 87 56 70 75 65 83AG-- 84 60 70 75 69 84 ABGH 86 53 69 77 57 85 AGI- 88 51 68 74 57 86ACGI 88 51 68 74 57 87 AHI- 85 56 68 75 64 88 ACHI 85 56 68 75 64 89ABHI 83 60 68 76 64

It will be noted in the above tables that a maximum of four diagnosticparameters are used in combination. This is because the Inventorsresearch has surprisingly indicated that using a combination of morethan four diagnostic features increases computational complexity withoutany substantial diagnostic performance gain.

If the clinician picks “accuracy” optimization then the best model willbe the top row of Table A3 (if the patient is 2 to 11 months of age) andtop Row of Table B3 (if the patient is 12 to 60 months of age). However,if the parameters that were entered by the clinician do not allow forthe top row #1 to be used then the microprocessor will check if row #2can be used and so on. Since the parameters that are listed (among thenine set out in table 1B) are common measurements usually it is notnecessary to descend far down the rows to find a first usable model.

For example,

Table 2B: 2-11 months: parameters:

#1 ACDI (breathing rate, runny nose, # days with runny nose, oxygensaturation)»Sens 91%, spec 70%. #13 ABCD (breathing rate, fever, runnynose, # days with runny nose)»sens 91%, spec 61% etc.

The flowchart of FIG. 8 details the model selection process thatmicroprocessor 3 performs under control of diagnostic application 6,within box 43 of the flowchart of FIG. 2. Using the data for tablesA1-A3 or B1-B3 (depending on age), the clinician's inputted preferencefor optimization for sensitivity, specificity or accuracy, and theinputted patient parameters, the device 1 automatically selects aparticular numbered diagnostic model using the procedure set out in FIG.9.

At box 45 (FIG. 2) the patient values that were entered for each of thediagnostic parameters that were selected are applied by microprocessor 3to the selected optimal diagnostic model. The screen that is depicted inFIG. 10 is presented on LCD touchscreen interface 11 whilst this processruns.

At box 47 microprocessor 3 operating under control of the diagnosticapplication 6 operates the LCD touchscreen interface 11 to present thediagnostic results to the carer as shown in the screen of FIG. 11.

Returning to the flowchart of FIG. 2, at box 49 the microprocessor 3 maysave the diagnosis results either locally to memory 5 or externally viaWAN/WLAN interface and antenna 29 to cloud server 33 via data network31. At box 50, if the diagnostic results indicate a disease state suchas pneumonia then the carer may use that information, as indicated inbox 52, to apply therapy to the patient 2, for example by organisinghospitalisation and/or providing antibiotics or antiviral medication.The clinician 4 may then indicate, at box 55, a desire to diagnoseanother patient in which case the procedure is repeated in respect ofanother patient.

As will be explained in the Appendices of this specification, theInventors developed the diagnostic models from quantitative test dataderived from a population of subjects including patients suffering fromrespiratory disease. The various models are arranged to classify apatient as “diseased” or “non-diseased” based upon the diagnosticparameters for the patient that are entered by the clinician. Apreferred approach to developing the models is by use of a logisticregression machine (LRM). Other types of classification decision enginesmay also be used, for example support vector machines (SVMs).

As explained in Section 1.2.2 of Appendix A herein, the use of coughsound features from just one or two coughs of a patient has been foundby the Inventors to provide a significant diagnosis performance gain.Where it is possible to record a cough sound from patient 2 by means ofmicrophone 25, those sounds can be processed by a cough featureextraction and processed along with other diagnostic parameters by asuitable one of the diagnostic models 20.

Referring now to FIG. 11A, there is shown a further embodiment of theinvention comprising diagnostic device 1 a, which as far as hardware isconcerned is the same as previously described diagnostic device 1,programmed with a variation of the diagnostic application 6 that waspreviously discussed being diagnostic application 6 a. Diagnosticapplication 6 a includes a cough feature extraction engine 24. The coughfeature extraction engine 24 includes instructions for themicroprocessor 3 to analyse a cough sound from the patient 2 that isreceived via microphone 25 and audio interface 21 and to generatecorresponding cough features. Cough feature extraction is known in theprior art and is described for example in international patentpublication No. WO 2013/142908 by present Inventor Abeyratne et al., thedisclosure of which is hereby incorporated by reference in its entirety.

Microprocessor 3 applies the cough features to diagnostic models 20 thatform part of the diagnostic application 6 a and which are configured totake into account other diagnostic parameter values that are input bythe clinician 4. This is done by adding cough features as agrouped-features to the existing lookup tables set out as Tables 2A-3C.The modified tables including the cough sounds are arranged with themodels in decreasing performance as before. Users, e.g. clinician 4, canselect the cough sounds as a measurement in the event that they are ableto collect sounds from patient 2. Otherwise they do not elect thatoption, just as in the case of other clinical signs. Then following thesame logic as discussed previously with respect to Tables 2A-3C thediagnostic device 1 is programmed by means of diagnostic application 6to pick the best model corresponding to the available measurements.

FIGS. 11B and 11C depict screens 54, 56 that are generated on LCDtouchscreen interface 11 by the microprocessor 3 under control of thediagnostic application 6 to prompt for the clinician 4 to capture atleast two cough sound samples from the patient 2. FIG. 11D depicts ascreen 57 that the microprocessor 3 generates on interface 11 forpresenting the results of the analysis to the clinician 4. As discussedin Appendix A, in other embodiments only a single cough sound has beenfound to assist in improving the performance of the machine diagnosis.

APPENDIX A

Childhood Pneumonia Diagnosis Using Clinical and Cough Features

1.1 Materials and Method 1.1.1 Study Population

A total of 222 children were recruited during the data collection: 93females and 129 males with a median age of 9 months and aninter-quartile range (IQR) of 4.25-20 months. Due to the absence of oneor more of the required parameters, we excluded 123 patients fromfurther consideration, leaving 99 children with complete data. Thedistribution of the 99 children is a close representation of the initial222 children recruited, as shown in Table 4.1. There were 52 childrenaged 2-11 months and 47 aged 12-60 months. Of the 99 children, 67 werepneumonia positive, whereas the remaining 32 experienced a mix ofasthma, bronchitis, bronchiolitis, heart disease, malnutrition,wheezing, etc. The non-pneumonia patients are the control set for thisstudy.

TABLE 4.1 Children representation between initial recruitment and actualnumber in study Initial Recruitment Model Building Category (n = 222) (n= 99) Age Median 10 months 11 months IQR 17 months 6 months DiagnosisRatio 2.47:1 2.1:1 Pneumonia 158 patients 67 patients Non-Pneumonia 64patients 32 patients Gender Ratio (M/F) 1.39:1 1.3:1 Male 128 patients56 patients Female 92 patients 43 patients Age Group   <2 Months 13subjects 0 subjects 2-11 Months 109 subjects 52 subjects 12-60 Months 100 subjects 47 subjects

1.1.2 Analysis of Data

The flow diagram 1200 presented in FIG. 12 details the process used inanalysing our data. For analysis, the dataset is split into two agegroups, 2-11 months and 12-60 months. This follows the WHO/IMCIprocedure which applies different breathing rate thresholds for each agegroup. Clinical and cough features from each group are tabled into afeature matrix for processing.

An LRM classifier was used to train a model for each featurecombination. This technique creates a non-linear model based on thefeature parameters and applies different weights to each parameter. TheLRM training process adjusts the weights such that each model is adaptedto differentiate pneumonia patients from non-pneumonia patients. Weexhaustively analysed feature combinations from one up to six featuresat a time. The process was divided into several phases: clinicalfeatures only, clinical plus cough features from one cough, and clinicalplus cough features from two coughs. Cough features from multiple coughswere averaged to a single set prior to analysis.

Leave one out (LOO) cross-validation was used to validate the results.At each iteration, one patient was designated the LOO person and therest became training set. An LRM model was created based on the trainingset and the cut-off threshold, carefully selected such that Sn≥90% withSp as high as possible. The model parameters were then fixed and used toevaluate the training set and LOO individual. Applying the trained modelon the training set provides the training performance such that the LOOperson is classified using the trained model. On the next iteration,another individual is designated LOO whilst the others become thetraining set. At the end of the iteration process where every patienthas been designated once as an LOO individual, the procedure performancewas calculated.

1.2 Results

In this section, we show the results of our analysis in three phases.Firstly, we compare the WHO/IMCI procedure performance in each age groupwith clinical features only. This is followed by comparison withclinical features plus features from one cough. Lastly, we compare theWHO/IMCI procedure results with our procedure using clinical plus coughfeatures from two coughs.

1.2.1 Clinical Only Features

The results in Table 4.2 show the WHO/IMCI classification and ourprocedure performance using only clinical features for each age group.The WHO/IMCI procedure performed with an Sn and Sp of 91.2% and 26.7%,respectively, for the 2-11 month age group. In the older age group, thecorresponding Sn and Sp are 83.3% and 11.8%. The best model in the 2-11month age group, using clinical features only, demonstrated an Sn and Spof 86.5% and 60.0%, respectively, using fever, days with cough,temperature and heart rate. In the 12-60 month age group, the best modelidentified used fever, temperature, runny nose and heart rate, with anSn and Sp of 90.0% and 76.6%, accordingly.

1.2.2 Clinical Plus One Cough

When features from just one cough were added to the mix, there wassignificant performance gain. For the 2-11 month age group, the bestmodel showed an Sn and Sp of 89.2% & 73.3%, respectively. This Sp valueis the equivalent of 275% that of the WHO/IMCI procedure with only asmall drop in the Sn. The numbers can be seen in Table 4.3. For the12-60 month age group, the best model using clinical features and onecough performed with an Sn and Sp of 90.0% & 82.4%, respectively.Compared to the WHO/IMCI performance, the Sp is equivalent to82.4/11.8=698% and the Sn is slightly higher than the WHO/IMCI's.

TABLE 4.2 Performance comparison between WHO and clinical featurecombinations Classification Performance (%) S_(n) S_(p) A_(cc) PPV NPV2-11 Months WHO 91.2 26.7 73.1 75.6 57.1 Fever + Days with cough +Temperature + Heart rate Training 91.7 64.9 84.0 86.6 76.0 LOO 86.5 60.078.8 84.2 64.3 Days with cough + Breathing rate + Temperature + Dayswith runny nose Training 91.9 60.2 82.8 85.1 75.0 LOO 86.5 53.3 76.982.1 61.5 Fever + Days with cough + Runny nose + Heart rate Training92.3 33.8 75.4 77.5 64.0 LOO 89.2 33.3 73.1 76.7 55.6 12-60 Months WHO83.3 11.8 57.5 62.5 28.6 Fever + Temperature + Runny nose + Heart rateTraining 92.0 67.9 83.2 83.5 82.8 LOO 90.0 70.6 83.0 84.4 80.0 Fever +Days with cough + Temperature + Heart rate Training 92.1 68.5 83.5 83.883.0 LOO 86.7 70.6 80.9 83.9 75.0 Breathing rate + Temperature + Runnynose + Days with runny nose Training 92.1 51.3 77.2 77.3 77.7 LOO 86.770.6 80.9 83.9 75.0 *LOO = leave one out, Sn = sensitivity, Sp =Specificity, Acc = accuracy, PPV = positive predictive value, NPV =negative predictive value

TABLE 4.3 Performance comparison between WHO and clinical plus one coughfeature combinations Classification Performance (%) S_(n) S_(p) A_(cc)PPV NPV 2-11 Months WHO 91.2 26.7 73.1 75.6 57.1 Days with cough +Temperature + Days with runny nose + 1 cough Training 91.7 79.7 88.391.8 79.6 LOO 89.2 73.3 84.6 89.2 73.3 Fever + Days with cough + 1 coughTraining 91.7 66.7 84.5 87.2 76.6 LOO 89.2 66.7 82.7 86.8 71.4 Days withcough + Breathing rate + Temperature + 1 cough Training 92.1 61.4 83.285.5 75.8 LOO 89.2 60.0 80.8 84.6 69.2 12-60 Months WHO 83.3 11.8 57.562.5 28.6 Temperature + Runny nose + Heart rate + 1 cough Training 92.082.2 88.4 90.1 85.3 LOO 90.0 82.4 87.2 90.0 82.4 Breathing rate + Dayswith runny nose + 1 cough Training 92.0 64.7 82.0 82.6 81.8 LOO 86.782.4 85.1 89.7 77.8 Fever + Runny nose + 1 cough Training 92.1 43.3 74.474.3 75.6 LOO 90.0 52.9 76.6 77.1 75.0 *LOO = leave one out, Sn =sensitivity, Sp = specificity, Acc = accuracy, PPV = positive predictivevalue, NPV = negative predictive value

TABLE 4.4 Performance comparison between WHO and clinical plus twocoughs feature combinations Classification Performance (%) S_(n) S_(p)A_(cc) PPV NPV 2-11 Months WHO 91.2 26.7 73.1 75.6 57.1 Days withcough + Breathing rate + Temperature + 2 coughs Training 91.8 85.7 90.094.1 80.9 LOO 89.2 86.7 88.5 94.3 76.5 Days with cough + 2 coughsTraining 91.7 80.5 88.5 92.1 79.8 LOO 89.2 80.0 86.5 91.7 75.0 Fever +Days with cough + Breathing rate + Days with runny nose + 2 coughsTraining 91.7 86.4 90.2 94.3 80.9 LOO 86.5 80.0 84.6 91.4 70.6 12-60Months WHO 83.3 11.8 57.5 62.5 28.6 Breathing rate + Temperature + Heartrate + 2 coughs Training 92.1 83.7 89.0 90.9 85.7 LOO 90.0 88.2 89.493.1 83.3 Fever + Runny nose + O2 Saturation + 2 coughs Training 92.074.5 85.6 86.5 84.1 LOO 90.0 76.5 85.1 87.1 81.3 Fever + Runny nose +BMI + 2 coughs Training 92.1 69.4 83.8 84.3 83.4 LOO 90.0 70.6 83.0 84.480.0 *LOO = leave one out, Sn = sensitivity, Sp = specificity, Acc =accuracy, PPV = positive predictive value, NPV = negative predictivevalue

1.2.3 Clinical Plus Two Coughs

Table 4.4 shows the LRM performance when clinical features are combinedwith features from two coughs. The best model for the 2-11 month agegroup utilises days with cough, breathing rate and temperature alongwith the cough features. This results in Sn and Sp of 89.2% and 86.7%,respectively. The Sp is equivalent to 325% that of the WHO/IMCIprocedure. In the 12-60 month age group, the best LRM model usesbreathing rate, temperature and heart rate along with cough features.The model performance was 90.0% Sn and 88.2% Sp. The Sp in this case isequivalent to 747% of the WHO/IMCI counterpart.

1.3 Discussion

The aim in this study was to investigate the use of common clinicalparameters in conjunction with cough sound analysis to diagnosechildhood pneumonia. A key requirement for this objective is to performbetter than the WHO/IMCI procedure. Our results have shown that addingcough sound analysis indeed helps identify childhood pneumonia betterthan just relying on clinical parameters. In this study, the WHO/IMCIprocedure performed with an Sn and Sp range of 83-91% and 12-27%,respectively. Our best performing models with two cough sounds were ableto classify pneumonia with an Sn and Sp range of 89-90% and 87-88%,respectively. These are the results from the LOO validation.

Cough is one of the most common symptoms in children with respiratoryproblems. The WHO/IMCI procedure takes into account the existence ofcough but does not utilise it fully. Our method extracts the importantfeatures from cough sound recordings and uses them to augment ourclinically based features in diagnosing pneumonia. We have shown this toprovide the best performance compared to using cough sounds or clinicalfeatures only.

The field of cough sound analysis is still relatively untouched, despitethe recent advancement in technology. We believe cough contains muchmore information regarding the physiology of the lung compared to whatis currently known. A diseased lung hypothetically could change thephysical characteristics of the lung and the characteristic sound ofcough in a way specific to the disease. In the case of childhoodpneumonia, we have shown in our previous studies that cough is afeasible parameter to use for diagnosis [7, 62]. The number of coughsused there was 15 per person, much higher that what has been used inthis thesis. We now have shown that, combined with clinical parameters,much fewer cough sounds are potentially required in order to produce anaccurate diagnosis.

1.4 Conclusion

We have demonstrated the potential benefits of combining clinical andcough features for childhood pneumonia diagnosis in resource-poorregions. The models we developed exhibited sensitivities of ˜90% andspecificities in the range of 325-747% times the WHO/IMCI procedure. Thekey parameters that worked best were the combination of breathing rate,temperature, heart rate and cough sound analysis. The clinical featuresare easily measurable and in the absence of key parameters, we couldswitch to other models and they can still perform well.

It should be noted that this study is currently based on a relativelysmall number of subjects (n=99) and there is yet to be established agold standard for pneumonia diagnosis. The reference standard we usedstems from a combination of clinical diagnosis by paediatricians aidedby auscultations, laboratory analysis, chest x-ray (where applicable)and the subject's response to treatment over the clinical course of thedisease.

End of Appendix A APPENDIX B Exhaustive Mathematical Analysis of SimpleClinical Measurements

In 1990, the World Health Organization (WHO) and UNICEF proposed the WHOcriteria for childhood pneumonia classification in resource-poorregions. This is the current de facto diagnostic method used bycommunity health workers in resource limited settings as a rapid lowcost alternative in frontline health facilities. Table 5 shows theWHO/IMCI guideline for pneumonia classification in resource poorregions.

TABLE 5 WHO/IMCI guidelines for pneumonia classification inresource-poor regions [35, 36] Sequence for Criteria ApplicationClassification 1. History of cough and/or difficult breathing Screenedin for next of less than 3 weeks duration procedure 2. Increasedrespiratory rate (tachypnea): Non-severe pneumonia ≥60/min if age <2months, ≥50/min if age 2-11 months, ≥40/min if age 12-60 months 3. Lowerchest wall indrawing Severe pneumonia 4. Cyanosis or inability to feedor drink Very severe pneumonia

The WHO/IMCI guideline dictates that if a patient exhibits symptoms ofcough/breathing difficulty, the patient is screened for the next step.

Breathing rate is taken and if it exceeds the limit (50 breaths perminute (bpm) for age 2-11 months, 40 bpm for age 12-60 months),non-severe pneumonia is declared. Danger signs such as lower chestindrawing and inability to feed or drink would put the patient in thesevere pneumonia category requiring immediate attention.

Researchers have generally recognized the limitations of the WHOcriteria, which are sensitive but not very specific^([9,15,16]). Overthe years, others have suggested the addition of fever^([17]), gruntingand nasal flaring^([8]), temperature and oxygen saturation^([18]).Rambaud-Althaus et al. proposed a combination of signs in a decisiontree format to improve clinical diagnosis accuracy^([8]). PneumoniaEtiology Research for Child Health (PERCH) investigators developed theirown standard interpretations of the symptoms and signs based on the WHOcriteria for a clinical case definition of pneumonia^([16]).

All these approaches make important contributions to dealing with theglobal burden of pneumonia, but largely suffer from the same type oflimitations afflicting the WHO criteria for resource-poor regions. Thesemethods also rely on health workers to perform measurements andinterpret data using basic binary decisions around fixed thresholds.

Methods Study Organization

The clinical data used for this study were collected by the Gadjah MadaUniversity-Sardjito Hospital, Yogyakarta, Indonesia, in partnership withThe University of Queensland, Brisbane, Australia. The data collectionbegan in December 2010 and continued until March 2014. The ethicscommittees of the Sardjito Hospital and The University of Queenslandapproved the study protocol. The inclusion/exclusion criteria are givenin Table 6 (Supplementary) below:

TABLE 6 Patient Recruitment Criteria Inclusion criteria Exclusioncriteria Patients with symptoms of Advanced disease where chestinfection. recovery is not At least 2 of: expected e.g. Cough Terminallung cancer Sputum Droplet precautions Increased breathlessness NIVrequired Temperature >37.5° C. Consent from parent/guardian Noinformed-consent

Patients are included if they exhibit any 2 symptoms of cough, sputum,increased breathlessness and temperature >37.5° C. Parental consent wassought prior to inclusion if the patient met the criteria and excludedif consent was not granted. Exclusion criteria also applied to patientsshowing symptoms of advanced disease, terminal lung cancer and/orrequiring a nasal drip IV, as these may skew the outcomes. As aprecaution, patients showing droplet-spread disease were also excluded.

Diagnostic Definitions

The reference diagnosis used in this study is the overall diagnosisprovided by the pediatricians on the basis of clinical presentation,laboratory tests, chest X-ray, and the clinical course of the disease.An X-ray was performed only on subjects clinically suspected ofpneumonia and, on other occasions, where there is clear need for it.

Therefore, not all our subjects underwent a chest X-ray.

Study Protocol

All children who satisfied the inclusion criteria were invited toparticipate in the study. Each child's history and clinical measurementswere recorded as part of the hospital admission process. Diagnosticoutcomes and all test results collected from the subjects in the courseof normal diagnosis/management of the disease were made available tothis study. Table 7 (Supplementary) lists some of the informationrecorded which was used for analysis in this paper.

TABLE 7 List of clinical parameters recorded at the time of admissionRecorded Recorded History details Examination details Fever Yes/No AgeMonths Days with fever Days Weight Kg Cough Yes/No Height m Days withcough Days Breathing rate Breaths/min Runny nose Yes/No Heart rateBeats/min Days with runny nose Days Temperature ° C. Breathingdifficulty Yes/No BMI Number Days with breathing Days Oxygen saturationPercentage difficulty Chest indrawing Yes/No

The test parameters included the existence of fever, cough, breathingdifficulty, runny nose, and chest indrawing as a binary yes/noobservation. It also included the following data as numbers: age,weight, height, breathing rate, temperature, BMI, oxygen saturation, andnumber of days suffering fever, cough, breathing difficulty, runny nose.Other diagnostic measures such as blood/sputum analysis and chest X-raywere performed only if the attending physician deemed it to benecessary.

Study Population

We recruited 222 children in total: 93 females, 129 males with a medianage of 9 months and an inter-quartile range (IQR) of 4.25-20 months. Ourpopulation came from subjects admitted to the hospital ward. Ourintention was to focus on the clinical parameters of interest indiagnosing pneumonia in resource poor regions. The dataset comprised 134children with the complete list of parameters specified earlier. Weexcluded 88 patients from further consideration due to the absence ofone or more of the required parameters. The distribution of the chosen134 children closely represented the initial 222 children recruited, asshown in Table 8.

TABLE 8 Children representation between initial recruitment and actualnumber in study Initial Recruitment Model Building Category (n = 222) (n= 134) Age Median 10 months 11 months IQR 17 months 21 months DiagnosisRatio 2.47:1 2.62:1 Pneumonia 158 patients 96 patients Non-Pneumonia 64patients 38 patients Gender Ratio (M/F) 1.39:1 1.27:1 Male 128 patients75 patients Female 92 patients 59 patients Age Group   <2 Months 13subjects 0 subjects 2-11 Months 109 subjects 71 subjects 12-60 Months 100 subjects 63 subjects

There were 71 and 63 children in the age groups 2-11 months and 12-60months, respectively. Of the 134 children, 96 were diagnosed withpneumonia, whereas the remaining 38 were a mix of asthma, bronchitis,bronchiolitis, heart disease, malnutrition, wheezing, etc.

The non-pneumonia group served as the control set for this study.

Analysis of Data

The flow diagram 1300 presented in FIG. 13 details the process used inanalysing our data. The data set is split into two age groups, 2-11months and 12-60 months.

Clinical features from each group are tabled into a feature matrix forprocessing.

Using a k-fold cross validation method, each age group was randomlysplit into k number of folds. An iterative process was then adopted inwhich one fold of data was retained as the testing set whilst the restof the data was used for training a logistic regression model (LRM). Agood general explanation of the logistic regression method used inmedical applications can be found in a paper by K. L. Sainani^([26]).

The LRM outputs the probability of the existence of pneumonia based onthe specified predictors, to which a cut-off threshold is applied tomake the output a binary decision. This threshold was carefully selectedfollowing a receiver operating characteristic (ROC) analysis to separatethe positive and negative pneumonia cases as cleanly as possible.

In the LRM design, we commenced by using one feature at a time andcomputing the performance of the resulting models. We then exhaustivelysearched all combinations of two features taken at a time. This processwas continued until we reached all 17 features taken at a time. In eachiteration, the trained models were evaluated according to theirsensitivity (S_(n)), specificity (S_(p)), accuracy (A_(cc)), and thearea under the curve (AUC). AUC was only available for the training datato set the diagnostics threshold.

Note that in each fold of the k-fold cross validation, the data set wasdivided into non-overlapping training and testing sets, and theperformance was estimated separately for both the training and testingsets. Each iteration generated k number of ROC curves and k sets oftraining and testing performance measures. Each iteration also generatedk number of trained LRM models. The trained LRM models were used tocalculate the performance of the training set. The LRM models were thenfixed, and used on the testing data set to compute the testingperformance and validate the trained model. Each set of LRM models wasconsidered final for its respective fold. Hence, for testingperformance, no AUC data were available.

The best performing models were chosen based on the means and standarddeviations (SD) of the training and testing performances. These numberswere calculated and are reported in the Results section. Similarly, theWHO criteria performance numbers were calculated for the testing setsand represented using their means and SDs. We then used the performancevalues to determine which parameters, in which combinations couldprovide the best diagnostic outcomes with the testing sets.

This process was iterated for each feature combination used. First weanalyzed the LRM performance of using one, two, and three features at atime. Next, we exhaustively analyzed all possible feature combinationswith up to 17 features being used at once. Table 9 shows the possiblecombinations for each number of features used in the creation of the LRMmodel.

TABLE 9 Total number of feature combinations possible out of 17 featuresavailable. No of No of possible Features Combinations 1 17 2 136 3 680 42,380 5 6,188 6 12,376 7 19,448 8 24,310 9 24,310 10 19,448 11 12,376 126,188 13 2,380 14 680 15 136 16 17 17 1 Total 131,071

Using one feature at a time gives 17 possible combinations, whereasusing all 17 features at a time would have only one possiblecombination. The number of possible combinations rises significantly inbetween. For example, the use of 8 features at a time results in 24,310combinations. In total, the number of models tested in this study iscomprised of 131,071 combinations. Given the large number of modelstested, the ROC curve analysis to find the best cut-off threshold foreach model becomes very important. We selected the threshold targeting aSn≥90% with S_(p) as high as possible. This also had the benefit oflowering the false discovery rate (FDR). As we mentioned earlier, ouraim is to improve the S_(p) of the WHO procedure, while maintaining highS_(n).

Results

In this section, we show the results of our analysis, starting from thecross-validation process and the WHO/IMCI procedure performance in ourpatient groups. We then describe the performance of our models andcompare with the WHO outcomes in the 2-11 month age group, followed bythe 12-60 month age group.

The Cross-Validation Technique

As detailed in Methods section, we use k-fold cross-validation to trainand evaluate our classifier models. In this study we set k=8, resultingin 8-9 children in each fold. Higher k values, such as the more commonlyused k=10, would result in 6-7 children in each fold. We deemed thisnumber as insufficient and decided k=8 gives better balance for thetesting data. Note that in each fold of the cross validation, trainingand testing sets are mutually exclusive, that is training and testtesting sets do not overlap.

WHO/IMCI Performance

We applied the WHO criteria (see Table I) to data in each fold of thek-fold cross validation data set, and computed the mean and the standarddeviation (SD) across all folds. Results are shown in Table 10.

TABLE 10 WHO criteria as applied to k-fold testing sets (Expressed inmean +/− standard deviation) Classification Performance (%) Age GroupS_(n) S_(p) A_(cc) PPV NPV 2-11 Mean 92.0 38.1 69.1 67.6 76.2 mth SD11.6 18.5 11.2 12.5 28.0 12-60 Mean 95.7 9.8 66.5 66.5 60.0 mth SD 7.613.1 12.8 15.2 49.0

As expected, WHO criteria yielded high S_(n) with relatively small SDacross both age groups, but at a poor specificity S_(p).

Our target is to maintain the high sensitivity of the WHO procedurewhile increasing the specificity. Next we describe the performance ofthe proposed method. As the analysis was done separately for each agegroup, we will begin by presenting the results for the 2-11 month agegroup.

Performance in the 2-11 Month Age Group

FIGS. 14A to 14C show mean S_(n) and S_(p) values for models using one,two, and three features at a time for the 2-11 month age group. Ourfeature set consisted of 17 observations/measurements as listed in Table11. The number of total combinations of one-feature taken at a time is17, leading to 17 LRM models with one feature as the input (FIG. 14A).Similarly, two features at a time and three features at a time give us136 and 680 LRM models respectively (FIG. 14B and FIG. 14C bottomframes). Overall, significant benefits were found in combining featuresup to four at a time in one LRM.

FIG. 15 shows the ROC curve analysis for 12 trained LRM models weselected for further consideration. Two models each are from single anddouble feature combinations, five from triple feature combinations, andthree with four feature combinations. The solid line in each framerepresents the mean ROC curve, formed over the k folds. Crosses on eachframe represent the SD of the S_(n) and 1-S_(p) at points shown. On eachframe of FIG. 15, we also graphically illustrate (see boxes) theperformance possible with the WHO/IMCI procedure for resource-limitedregions. The centre of the box indicates the mean performance, and theheight and width of the box represent SD. Table 11 shows the trainingand testing performance numbers for the 12 models.

TABLE 11 Performance comparison between various models for diagnosingpneumonia in children aged 2-11 months (AUC = Area Under Curve, CO =Cut-Off threshold). Testing Training Performance (%) Performance (%)Features S_(n) S_(p) A_(cc) AUC CO S_(n) S_(p) A_(cc) Temperature Mean93.2 11.9 59.9 71.7 38.4 88.8 11.3 58.7 SD 1.4 9.8 3.9 2.5 3.7 13 12.212.2 Breathing rate Mean 93.5 35.4 69.8 74.9 35.6 91.3 35.2 65.7 (BR) SD1.5 12 4.3 2.2 2.5 13 29.8 17.1 Chest indrawing Mean 97.6 34.4 71.8 6667.9 97.9 32.9 71.6 SD 1 3.4 1.6 1.8 1.8 5.9 23.7 10.7 Fever + Mean 93.244.5 73.2 82.1 33.5 86 50.2 69.1 Breathing rate SD 1.5 11.2 4.7 2.6 3.717.9 26.6 18.2 Oxygen(O2) saturation + Mean 91.8 37.9 69.8 75.0 37.291.2 35.2 65.7 Breathing rate SD 0.3 11.6 5.0 2.3 3.4 13.0 29.8 17.0 Age(months) + Fever + Mean 91.8 63.7 80.3 83.3 39.9 86 66.7 76.6 Breathingrate SD 0.3 7.9 3.4 2.4 5.1 17.9 22.5 16.8 Age (months) + Fever + Mean91.8 63.7 80.3 81.2 41.3 83.5 67.3 76.1 Days with cough SD 0.3 4.5 2.41.9 10.6 14.5 23.9 18.5 Fever + Temperature + Mean 91.8 62.1 79.7 82.646 90.6 57.7 77.8 Chest indrawing SD 0.3 1.2 0.8 1.8 5.6 13.7 13 10.6Fever + O2 saturation + Mean 92.8 64 81.1 77.9 44.1 88.1 61.9 77.6 Chestindrawing SD 1.5 3 1.6 1.9 8.7 13.6 8.7 10.9 Fever + Breathing rate +Mean 91.8 59.2 78.5 83.6 41.4 86 56.3 73.6 Chest indrawing SD 0.3 4.72.2 2.1 4.3 17.9 10.1 14.1 Fever + Days with cough + Mean 92.2 65.9 81.584.7 59.6 86.0 63.5 76.3 Heart rate + Chest indrawing SD 1.0 6.5 2.6 2.67.9 15.5 18.6 13.3 Runny nose + Days with Mean 91.8 77.4 85.9 88.1 51.091.3 70.2 82.0 runny nose + BR + Temp SD 0.3 6.1 2.5 2.1 6.1 13.0 22.89.3 Runny nose + Days wish Mean 91.8 64.0 80.5 83.5 41.5 91.5 66.0 80.1runny nose + BR + Heart rate SD 0.3 4.6 2.0 2.2 2.6 9.2 26.3 12.1

When a single feature is used to create the LRM for the 2-11 month agegroup, the best features in terms of testing performance were breathingrate (S_(n) of 91% and S_(p) of 35%) and chest in-drawing (S_(n) of 98%and S_(p) of 33%). These numbers closely matched the performance of theWHO criteria, as it also relies on the same features for childhoodpneumonia classification.

Individually, the breathing rate and temperature exhibit the highest AUCin the training performance. However, the temperature model shows highS_(n) and lower S_(p) compared to the WHO criteria, as opposed tobreathing rate model which has comparable numbers. On the testingdataset, both models demonstrate high S_(n) with little SD, but the SDsof the S_(p) vary wildly, rendering both models unusable by themselves.This suggests that the WHO criteria are still more reliable whencompared to single feature LRM models.

The use of two features at a time boosts the S_(p) to 50% for certainfeature combinations while maintaining S_(n) around 90%. This is asignificant improvement from the S_(p) of the best single feature model.The best performers are models using breathing rate with oxygensaturation, and, breathing rate with fever. Both feature combinationsexhibit high AUC (75-82%) in training.

We continued to add features further until the optimal LRM featurecombinations were found. On the three simultaneous feature models, theoverall testing performances are higher than the double feature ones.Mean S_(n) levels remain largely the same around 90% and mean S_(p)levels are on average 30% higher than the double feature models. The SDlevels for S_(n) is unchanged but for S_(p) is 44% smaller. The bestperforming model for this category includes fever, oxygen saturation andchest in-drawing as parameters, achieving a S_(n) value of 88.1±13.6%and an S_(p) value of 61.9±8.7%. Compared to WHO/IMCI procedure (S_(n)and S_(p) of 92.0±11.6% and 38.1±18.5%, respectively), the mean S_(n) is4% lower while S_(p) is 62% higher. The SDs are 17% larger for S_(n) and53% smaller for S_(p) compared to WHO. Thus, the best triple featuremodel performs much better than WHO criteria in terms of S_(p), with asmall loss of S_(n).

Further improvements in classification performance are found using fourfeatures at a time. The best performing model uses the existence ofrunny nose, number of days with runny nose, breathing rate andtemperature (91.3±13.0% S_(n) and 70.2±22.80% S_(p)). The mean S_(n) ison a par with the WHO results, and the mean S_(p) is 84% higher. The SDfor both S_(n) and S_(p) are, however, slightly higher compared toWHO/IMCI procedure. The second best performing model uses runny nose,days with runny nose, breathing rate, and heart rate at S_(n) of91.5±9.2% and S_(p) of 66.0±26.3%. The mean S_(n) is also on a par withthe WHO results while S_(p) is 73% higher. For SDs, they are 20% smallerfor S_(n) and 42% larger for S_(p) compared to WHO.

Performance in the 12-60 Month Age Group

For the 12-60 month age group, the same process is repeated, startingwith observation of the ROC curves from 12 trained LRM models chosen forcomparison, as shown in FIG. 16, which is a ROC curve analysis for theage group of 12-60 month. The solid line in each frame represents themean ROC curve, formed over k-iterations. Crosses on each framerepresent the SD of the S_(n) and 1-S_(p) at points shown. On eachframe, the performance of WHO/IMCI procedure for resource-limitedregions is graphically illustrated by the crossed boxes. The center ofthe box indicates the mean performance, and the height and width of thebox represent SD.

TABLE 12 Performance comparison between trained models and WHO criteriafor children aged 12-60 months (AUC = Area Under Curve, CO = Cut-Offthreshold). Testing Training Performance (%) Performance (%) FeaturesS_(n) S_(p) A_(cc) AUC CO S_(n) S_(pA) A_(cc) Temperature Mean 93.0 10.764.4 70.4 46.6 88.0 19.2 61.6 SD 1.6 13.6 3.9 2.6 3.2 14.0 35.0 35.0Breathing rate Mean 93.0 41.5 75.1 74.9 42.6 88.2 45.2 71.2 (BR) SD 1.69.4 2.9 2.8 4.2 14.6 32.0 15.2 Chest indrawing Mean 97.6 32.0 74.6 64.872.3 97.5 32.3 74.3 SD 1.0 4.9 3.0 2.5 3.0 7.1 35.8 20.6 Fever + Mean92.0 49.6 77.3 82.0 41.4 88.3 44.2 67.9 Breathing rate SD 1.1 10.5 3.12.4 5.9 15.2 47.7 21.1 Oxygen(O2) saturation + Mean 91.6 47.0 76.0 79.346.5 88.2 57.7 72.2 Breathing rate SD 0.4 6.5 3.2 2.7 6.5 14.6 31.3 17.5Age (months) + Fever + Mean 91.6 58.4 80.0 81.5 46.9 88.3 70.6 75.9Breathing rate SD 0.4 10.3 3.6 2.5 7.2 15.2 33.5 14.9 Age (months) +Fever + Mean 91.6 60.4 80.7 79.3 53.5 84.3 59.6 74.6 Days with cough SD0.4 4.3 2.0 2.2 9.4 21.2 37.6 13.4 Fever + Temperature + Mears 92.0 57.780.0 80.6 51.8 92.1 51.3 79.2 Chest indrawing SD 0.9 3.8 1.4 2.6 9.715.8 39.0 16.5 Fever + Breathing rate + Mean 91.6 56.4 79.4 82.3 49.088.3 58.1 74.3 Chest indrawing SD 0.4 9.6 3.3 2.4 9.1 15.2 39.2 17.0Fever + Breathing rate Mean 92.0 61.1 81.2 85.5 42.1 87.9 74.8 77.5Temperature SD 0.9 7.9 3.0 1.7 5.1 15.4 30.2 16.9 Fever + Days withcough + Mean 91.6 68.3 83.4 82.7 62.1 94.0 74.0 84.4 Heart rate + Chestindrawing SD 0.3 6.6 2.7 3.4 7.7 12.1 23.3 8.8 Runny nose + Days withMears 91.6 74.0 85.5 87.5 52.3 91.4 71.9 87.5 runny nose + BR + Temp SD0.3 4.9 1.7 2.8 9.8 12.1 36.4 9.2 Runny nose + Days with Mean 31.6 61.881.2 84.0 47.6 85.9 59.4 74.6 runny nose + BR + Heart rate SD 0.3 3.21.6 2.8 3.3 21.2 37.6 17.7

The two best performing models for both age groups are compared in FIG.17.

For the single feature category, breathing rate and chest in-drawing(individually) still exhibit the best performance in general. TheWHO/IMCI procedure implementation for this age group demonstrates S_(n)of 95.7±7.6% and S_(p) of 9.8±13.1%. The best double feature LRM modelsin this age group also include breathing rate as a parameter. The besttwo models are breathing rate with fever and breathing rate with oxygensaturation.

For triple features, the best testing performance was observed whenusing fever, temperature, and chest in-drawing. This combination reacheda S_(n) of 92.1±15.8% and S_(p) of 51.3±39.0% for testing performance.The mean S_(n) is still comparable to the WHO criteria with 3%disparity, but the mean S_(p) of the LRM model is 423% higher.

The SD of the S_(n) is 107% larger compared to WHO, and for S_(p) it is197% greater. The best S_(p) in this category is found in thecombination of fever, breathing rate and temperature with S_(p) of74.8%±30.2%. This is a 663% increase of mean S_(p) over WHO with 130%increase in SD. The mean S_(n) value is also 8% lower compared to WHOresults while SD remains 100% higher.

In models with four features, the best performing model uses theexistence of fever, number of days with cough, heart rate, and existenceof chest in-drawing (S_(n) of 94.0±12.1% and S_(p) of 74.0±23.3%). Themean S_(n) is 2% lower than the WHO performance and the mean S_(p) is655% higher. The second best performing model utilizes runny nose, dayswith runny nose, breathing rate, and temperature with S_(n) and S_(p) of91.4±12.1% and 71.9±36.4%, respectively.

Recurrent Features in Best Performing Models

Following the recurrent appearance of certain features amongst the bestperforming LRMs in all feature combinations, we decided tosystematically explore these features in order to rank the mostsignificant features out of the 17 considered.

We set a threshold S_(n) of 90% and S_(p) of 70% on the mean testingperformance for all possible combinations, from using one feature at atime to 17 features, and found 20 models that meet the criteria (eightmodels from the 2-11 month age group and 12 from the 12-60 age monthgroup, respectively). Table 13 shows the number of recurrence for eachfeature within the top 20 feature combinations.

TABLE 13 Number of feature occurrence in the models showing >90% Snand >70% Sp.

60 No Feature Name mths mths 1 ‘Ageinmonths' 0 0 2 ‘Fever’ 0 4 3‘Dayswithfever’ 0 0 4 ‘Cough’ 0 1 5 ‘Dayswithcough’ 0 4 6 ‘Runnynose’ 88 7 ‘Dayswithrunnynose’ 8 8 8 ‘Breathingdifficulty’ 0 1 9‘Dayswithbreathingdifficulty’ 0 0 10 ‘Weight’ 0 0 11 ‘Height’ 0 0 12‘Breathingrate’ 8 8 13 ‘Heartrate’ 0 1 14 ‘Temperature’ 8 8 15 ‘BMI’ 4 516 ‘O2Saturation’ 4 4 17 ‘Chestindrawing’ 4 8

indicates data missing or illegible when filed

Several measurements such as the breathing rate and observations such asthe existence of runny nose are dominantly present as recurrent featuresin good models.

Discussion

One particular aim of this study was to explore if common clinicalobservations and measurements could be utilized to diagnose pneumonia atspecificities higher than possible with the WHO procedure, whilemaintaining the sensitivity of at least 90%. Our results haveillustrated that this is indeed possible. Our best performing modelsdemonstrated a sensitivity of 91% while achieving an S_(p) in the rangeof 70-72% depending on the age of the subjects. These numbersrepresented 84-655% increase in S_(p) compared to the WHO/IMCIprocedure, which had S_(p) ranging between 10-38%. Our results are basedon k-fold cross validation, and the reported outcomes are thus not onthe same data used to train a particular model.

The number of clinical observations and measurements needed to achieve adesired performance provides useful insight in designing clinicalprotocols targeting resource-poor areas. Results we obtained indicatedthat our single feature models perform similar to the WHO/IMCIprocedure. Addition of second, third and fourth features significantlyimprove S_(p) while S_(n) continues to hold above 90%. Beyond fourfeatures, the calculation complexity rises without any performance gain.

One important contribution of this paper is the identification of mostimportant clinical features and measurements that may substantiallyincrease the accuracy of diagnosing pneumonia in resource-poor regions.We surveyed our exhaustive model database for the repeated appearance offeatures in models satisfying S_(n)>90% and S_(p)>70%.

The breathing rate appeared as a feature in 16 models across both agegroups. Oxygen saturation and chest in drawing too were importantparameters appearing respectively in 8 and 12 models out of a total of20. The significance of these measurements are well known among themedical and research communities. Our work uncovered two parameters ofpotential significance; “the existence of runny nose” and the “number ofdays with runny nose” both of which appeared in 16 out of 20 models,just like the breathing rate. The “existence of fever” also presented asa frequent parameter (4 out of 20 models) for the age group 12-60months.

Breathing rate is the main measurement used in the WHO/IMCI procedure todiagnose pneumonia. While it appears an easy parameter to measure, ithas been found difficult to achieve in resource-poor regions. Therefore,a major fraction of the global pneumonia diagnosis resources areallotted to improving technologies and protocols to measure thebreathing rate^([nn]). Without a reliable breathing rate measurement,the WHO/IMCI methods cannot be used in the field.

Our results suggest that while breathing rate is an important parameter,it is not essential to diagnose pneumonia. For instance, our model usingthe four features age, existence of fever, existence of cough and daysof cough was capable of S_(n)=83.5±14.5% and S_(p)=67.3±23.9%respectively, for the 2-11 month age group. In the other age group, thismodel exhibited S_(n) and S_(p) of 91.7±17.8% and 51.0±34.6%,respectively. Among two-feature models, the combination using fever anddays with cough resulted in S_(n) and S_(p) of 90.2±14.0% and44.3±25.9%, respectively, for the 2-11 month age group. For the olderage group, the model performed with S_(n)=85.5±15.2% andS_(p)=41.9±47.7%. These results corroborate our previous observationthat breathing rate may not add additional value when mathematicalfeatures derived from cough sounds are available for diagnosingpneumonia³¹.

Recently there has been a renewed interest in the use of pulse oximetryin reducing childhood pneumonia mortality in resource-poorsettings^([27-29]). Hypoxemia is a diagnostic indicator for severepneumonia and swift access to oxygen treatment could improve theprognosis, when available. In our exhaustive model building process, wefound 8 of the 20 best models included oximetry as a feature. Oximetrycan be a highly useful feature. However, our results suggest that we cansubstitute, in its place, simpler feature combinations when a pulseoximeter is not available in the field. Examples features are theexistence of runny nose and the days of runny nose.

The WHO/IMCI criteria for resource-poor regions have been designed to behighly sensitive to detect pneumonia (94% for those aged <24 months, 62%for ≥24 months)^([17]). A high number of false positive results alsooccur, reducing the specificity of the method (16-20%)^([17]). In two ofour previous works on children, we have seen WHO/IMCI performing at asensitivity of 83% and a specificity of 47% (n=91)^([25,31]). TheWHO/IMCI criteria works well when applied by doctors in conjunction withclinical and radiological analysis, giving performances of 77-81%sensitivity and 77-80% specificity^([32]). These numbers are comparablewith what we obtained in this paper, though our method did not uselaboratory or radiological measurements.

Low specificity of the WHO criteria can lead to rising antimicrobialresistance in communities and render antibiotics ineffective. It alsowastes rare drug stocks and delays early treatment opportunities fordiseases with symptom overlap (e.g. malaria)^([10,33]). In low resourcesettings where only WHO/IMCI criteria are available, as many as 30% ofcases had symptoms compatible with both malaria and pneumonia,necessitating dual treatment^([34]). One of these treatments could beredundant. The method presented in this paper could potentially helpwith these issues by producing more accurate results, even in theabsence of key parameters such as breathing rate.

The approach we took in this paper is unique. We systematicallyexhausted all possible feature combinations in our set of 17 features.Altogether we built and tested 131,071 models, each using differentfeature combinations. In the literature there are instances whereWHO/IMCI procedure was augmented with one or two other handpickedclinical features (e.g. fever, oximetry) targeting manualinterpretation. For instance, Cardoso et al in their 2010 study^([17])added fever to WHO/IMCI procedure and illustrated the specificityincreased up to 44% (age group <24 month) and 50% (age group 24-60months). However, the sensitivity was reduced below that of WHO/IMCI. Inparticular, in the age group 24-60 months, neither the original WHO/IMCInor the modified method could achieve sensitivity above 62%. The methodwe proposed can achieve a sensitivity above 90% while maintaining thespecificity at the range 70-72%. No manual interpretation of features isnecessary, and our method can provide a decision device.

In an independent development, Naydenova et al. published results on amethod of combining several features using a machine learningapproach^([30]). They reported oxygen saturation, temperature, breathingrate and heart rate as leading to the best performance in their model(sensitivity 96.6%, specificity 96.4%). In our work, the same featurecombination resulted in a much inferior performance (sensitivity 88.8%,specificity 40% in the age group 2-11 months; sensitivity 82.7% andspecificity 35.4% in the age group 12-60 months).

One critical difference between our method and the one byNaydenova^([30]) is that they used healthy people as Control Subjectswhile we used children with respiratory symptoms satisfying inclusioncriteria as our Control Subjects. Our Control Subjects were children whovisited the hospital seeking treatment for illnesses with symptomsshared with pneumonia, but the medical diagnosis was they had differentdiseases. The research problem we explored was completely different fromthe one examined by Naydenova^([30]) and the results are thus notcomparable. Separating normal children from pneumonia subjects is a muchsimpler problem compared to identifying pneumonia subjects from a groupof children with a range of respiratory illnesses.

CONCLUSION

We have developed a method using logistic regression modelling todiagnose pneumonia based on various clinical features commonly recordedfrom patients. The LRM models we developed retain the high sensitivityof the WHO/IMCI procedure while increasing its mean specificity by 84%for the 2-11 month age group and 655% for the 12-60 month age group.

This study is currently limited by the number of subjects (n=134)involved in the study as well as the way pneumonia was diagnosed. Thereference standard used in this study is the overall clinical diagnosisaided by auscultation, laboratory analysis and radiography (when deemedclinically necessary by the attending physician) and the clinical courseof the subject's response to treatment. Due to the need limit radiationexposure to children, x-ray imaging was not performed on all subjects inthe study.

REFERENCES

The disclosure of each of the following documents is hereby incorporatedin its entirety by reference.

-   [1] Fullman N, Lim S, Dieleman J, Greenslade L, Graves C, Huynh C,    et al. Pushing the Pace: Progress and Challenges in Fighting    Childhood Pneumonia. (IHME) IfHMaE, editor. Seattle, Wash.: IHME;    2014.-   [2] Liu L, Oza S, Hogan D, Perin J, Rudan I, Lawn J E, et al.    Global, regional, and national causes of child mortality in 2000-13,    with projections to inform post-2015 priorities: an updated    systematic analysis. The Lancet. 2015; 385(9966):430-40.-   [3] UN. We Can End Poverty: Millenium Development Goals and    Beyond 2105. 2015; Available from:    http://www.un.org/millenniumgoals/childhealth.shml.-   [4] Bryce J, Black R, Victora C. Millennium Development Goals 4 and    5: progress and challenges. BMC Medicine. 2013; 11(1):225.-   [5] UN. The Millenium Development Goals Report 2014. New York: UN;    2014.-   [6] Walker C L, Rudan I, Liu L, Nair H, Theodoratou E, Bhutta Z A,    et al. Global burden of childhood pneumonia and diarrhoea. Lancet.    2013; 381(9875):1405-16.-   [7] Madhi S A, Wals P D, Grijalva C G, Grimwood K, Grossman R,    Ishiwada N, et al. The Burden of Childhood Pneumonia in the    Developed World: A Review of the Literature. The Pediatric    Infectious Disease Journal. 2013; 32(3):e119-e27.-   [8] Rambaud-Althaus C, Althaus F, Genton B, D'Acremont V. Clinical    features for diagnosis of pneumonia in children younger than 5    years: a systematic review and meta-analysis. The Lancet Infectious    Diseases. 2015; 15(4):439-50.-   [9] Qazi S, Were W. Improving diagnosis of childhood pneumonia. The    Lancet Infectious Diseases. 2015; 15(4):372-3.-   [10] WHO. Antimicrobial resistance: global report on    surveillance 2014. Geneva: World Health Organization; 2014.-   [11] Lynch T, Bialy L, Kellner J D, Osmond M H, Klassen T P, Durec    T, et al. A Systematic Review on the Diagnosis of Pediatric    Bacterial Pneumonia: When Gold Is Bronze. PloS one. 2010;    5(8):e11989.-   [12] Chang A B, Ooi M H, Perera D, Grimwood K. Improving the    Diagnosis, Management, and Outcomes of Children with Pneumonia:    Where are the Gaps? Frontiers in Pediatrics. 2013; 1:29.-   [13] Esposito S, Principi N. Unsolved problems in the approach to    pediatric community-acquired pneumonia. Curr Opin Infect Dis. 2012;    25(3):286-91.-   [14] Rudan I, El Arifeen S, Bhutta Z A, Black R E, Brooks A, Chan K    Y, et al. Setting Research Priorities to Reduce Global Mortality    from Childhood Pneumonia by 2015. PLoS Med. 2011; 8(9):e1001099.-   [15] WHO. Consultative meeting to review evidence and research    priorities in the management of ARI. Geneva: World Health    Organization; 2004.-   [16] Scott J A G, Wonodi C, Moïsi J C, Deloria-Knoll M, DeLuca A N,    Karron R A, et al. The Definition of Pneumonia, the Assessment of    Severity, and Clinical Standardization in the Pneumonia Etiology    Research for Child Health Study. Clinical Infectious Diseases: An    Official Publication of the Infectious Diseases Society of America.    2012; 54(Suppl 2):S109-S16.-   [17] Cardoso M-RA, Nascimento-Carvalho C M, Ferrero F, Alves FtM,    Cousens S N. Adding fever to WHO criteria for diagnosing pneumonia    enhances the ability to identify pneumonia cases among wheezing    children. Arch Dis Child. 2011; 96(1):58-61.-   [18] Wingerter S L, Bachur R G, Monuteaux M C, Neuman M I.    Application of the World Health Organization Criteria to Predict    Radiographic Pneumonia in a US-based Pediatric Emergency Department.    The Pediatric Infectious Disease Journal. 2012; 31(6):561-4.-   [19] Dreiseitl S, Ohno-Machado L. Logistic regression and artificial    neural network classification models: a methodology review. Journal    of Biomedical Informatics. 2002; 35(5-6):352-9.-   [20] Stoltzfus J C. Logistic Regression: A Brief Primer. Academic    Emergency Medicine. 2011; 18(10):1099-104.-   [21] Shorr A F, Zilberberg M D, Reichley R, Kan J, Hoban A, Hoffman    J, et al. Readmission Following Hospitalization for Pneumonia: The    Impact of Pneumonia Type and Its Implication for Hospitals. Clinical    Infectious Diseases. 2013.-   [22] Aliberti S, Di Pasquale M, Zanaboni A M, Cosentini R, Brambilla    A M, Seghezzi S, et al. Stratifying Risk Factors for    Multidrug-Resistant Pathogens in Hospitalized Patients Coming From    the Community With Pneumonia. Clinical Infectious Diseases. 2012;    54(4):470-8.-   [23] Teka Z, Taye A, Gizaw Z. Analysis of risk factors for mortality    of in-hospital pneumonia patients in Bushulo Major Health Center,    Hawassa, Southern Ethiopia. Science. 2014; 2(5):373-7.-   [24] Swarnkar V, Abeyratne U, Chang A, Amrulloh Y, Setyati A,    Triasih R. Automatic Identification of Wet and Dry Cough in    Pediatric Patients with Respiratory Diseases. Ann Biomed Eng. 2013;    41(5):1016-28.-   [25] Kosasih K, Abeyratne U R, Swarnkar V, Triasih R. Wavelet    Augmented Cough Analysis for Rapid Childhood Pneumonia Diagnosis.    Biomedical Engineering, IEEE Transactions on. 2015; 62(4):1185-94.-   [26] Sainani K L. Logistic Regression. PM&R. 2014; 6(12):1157-62.-   [27] Floyd J, Wu L, Hay Burgess D, Izadnegandar R, Mukanga D, Ghani    A C. Evaluating the impact of pulse oximetry on childhood pneumonia    mortality in resource-poor settings. Nature. 2015; 528(7580):553-59.-   [28] Ginsburg A S, Delarosa J, Brunette W, Levari S, Sundt M, Larson    C, et al. mPneumonia: Development of an Innovative mHealth    Application for Diagnosing and Treating Childhood Pneumonia and    Other Childhood Illnesses in Low-Resource Settings. PloS one. 2015;    10(10):e0139625.-   [29] Emdin C A, Mir F, Sultana S, Kazi A, Zaidi A M K, Dimitris M C,    et al. Utility and feasibility of integrating pulse oximetry into    the routine assessment of young infants at primary care clinics in    Karachi, Pakistan: a cross-sectional study. BMC Pediatrics. 2015;    15(1):1-11.-   [30]. Naydenova E, Tsanas A, Casals-Pascual C, Vos M D, editors.    Smart diagnostic algorithms for automated detection of childhood    pneumonia in resource-constrained settings. Global Humanitarian    Technology Conference (GHTC), 2015 IEEE; 2015 8-11 Oct. 2015.-   Abeyratne U R, Swarnkar V, Setyati A, Triasih R. Cough Sound    Analysis Can Rapidly Diagnose Childhood Pneumonia. Ann Biomed Eng.    2013; 41(11):2448-62. Mulholland E K, Simoes E A, Costales M O,    McGrath E J, Manalac E M, Gove S. Standardized diagnosis of    pneumonia in developing countries. Pediatr Infect Dis J. 1992;    11(2):77-81. Epub 1992/02/01.-   Thaver D, Ali S A, Zaidi A K. Antimicrobial resistance among    neonatal pathogens in developing countries. Pediatr Infect Dis J.    2009; 28(1 Suppl).-   Källander K, Nsungwa-Sabiiti J, Peterson S. Symptom overlap for    malaria and pneumonia—policy implications for home management    strategies. Acta Tropica. 2004; 90(2):211-4.-   WHO. Acute respiratory infections in children: Case management in    small hospitals in developing countries: A manual for doctors and    other senior health workers. Geneva: World Health Organization;    1990.-   Puumalainen T, Quiambao B, Abucejo-Ladesma E, Lupisan S,    Heiskanen-Kosma T, Ruutu P, et al. Clinical case review: A method to    improve identification of true clinical and radiographic pneumonia    in children meeting the World Health Organization definition for    pneumonia. BMC Infect Dis. 2008; 8(1):95.-   {nn} group:-   nn1. The Acute Respiratory Infection Diagnostic Aid (ARIDA) Project,    Unicef. http://www.unicef.org/innovation/innovation_81722.html    (accessed on Aug. 3, 2016).-   nn2. World's first pneumonia innovations summit unveils next    generation prevention, diagnostic and treatment innovations, World    Pneumonia Day-2015,    http://www.malariaconsortium.org/news-centre/worlds-first-plieumenia-innovations-summit-unveils-next-generation-prevention-diagnostic-and-treatment-innovations.htm    (accessed on Aug. 3, 2016).-   nn3. Pneumonia Diagnostics Project, Malaria Consortium,    http://www.malariaconsortium.org/projects/pneumonia-diagnostics    (accessed on Aug. 3, 2016).

End of Appendix B

In compliance with the statute, the invention has been described inlanguage more or less specific to structural or methodical features. Theterm “comprises” and its variations, such as “comprising” and “comprisedof” is used throughout in an inclusive sense and not to the exclusion ofany additional features. It is to be understood that the invention isnot limited to specific features shown or described since the meansherein described herein comprises preferred forms of putting theinvention into effect. The invention is, therefore, claimed in any ofits forms or modifications within the proper scope of the appendedclaims appropriately interpreted by those skilled in the art.

Throughout the specification and claims (if present), unless the contextrequires otherwise, the term “substantially” or “about” will beunderstood to not be limited to the value for the range qualified by theterms.

Any embodiment of the invention is meant to be illustrative only and isnot meant to be limiting to the invention. Therefore, it should beappreciated that various other changes and modifications can be made toany embodiment described without departing from the spirit and scope ofthe invention.

1. A method for automatically providing a carer of a patient with adisease state diagnosis of the patient including the steps of: providinga diagnostic application software product including a multiplicity ofdiagnostic models derived from investigation of a population containingdisease state positive and non-disease state subjects; operating aninput/output interface of an electronic device including a memorystoring the software product to prompt the carer for identification of anumber of disease diagnostic parameters and values therefor for thepatient; operating a processor of the electronic device to select one ofthe diagnostic models from the memory based on the identified diseasediagnostic parameters; operating the processor to apply the values ofthe disease diagnostic parameters to the selected diagnostic model; andpresenting a diagnosis to the carer on the input/output interface basedupon results of the application of the parameters to the selecteddiagnostic model for the carer to use in providing therapy to thepatient.
 2. A method according to claim 1, wherein said diagnosticparameters comprise: breathing rate, existence of fever, existence ofrunny nose, number of days with runny nose, number of days with cough,existence of chest indrawing, temperature, BMI (body mass index), andoxygen saturation level;
 3. A method according to claim 1, includingselecting one of the diagnostic models with reference to one or morelook up tables correlating diagnostic performance of models againstavailable diagnostic parameters.
 4. A method according to claim 1,including prompting for user choice of a diagnostic model optimized for“sensitivity”, “specificity” or “accuracy” wherein the method includesoperating the electronic device to select one of the diagnostic modelstaking into account the optimization choice.
 5. A method according toclaim 1, including operating the device to determine if the values ofdiagnostic parameters indicate patient danger signs.
 6. A methodaccording to claim 5, including checking if the diagnostic values forthe patient indicate that the patient is presenting general danger signsaccording to World Health Guidelines.
 7. A method according to claim 1,including saving diagnostic results to a remote server wherebydiagnostic results may be saved and compared from a plurality ofdiagnostic devices.
 8. A method according to claim 1, includingprompting for recording of at least one patient cough sound.
 9. A methodaccording to claim 8, including prompting for no more than two patientcough sounds.
 10. A method according to claim 8, including applying theat least one patient cough sound to a cough feature extraction engine ofthe diagnostic application to generate patient cough features.
 11. Amethod according to claim 10, wherein the patient cough features areapplied to the diagnostic model to assist the diagnosis.
 12. A methodaccording to claim 1, wherein the diagnostic parameters comprisebreathing rate, temperature, heart rate and cough sound analysis.
 13. Adiagnostic device arranged to prompt a clinician to input diagnosticinformation for a patient and further arranged to automatically presenta diagnosis to the clinician, the diagnostic device including: anelectronic memory storing instructions comprising a diagnosticapplication software product including a plurality of diagnostic modelsderived from investigation of pneumonia-positive and non-pneumoniasubjects; an electronic processor in communication with the electronicmemory for executing the instructions; a user interface responsive tothe electronic processor for the processor to prompt for and receive thediagnostic information; wherein the processor is configured by thediagnostic application in use to present a diagnosis of the patient withthe user interface by applying the diagnostic information to at leastone of said diagnostic models.
 14. A diagnostic device according toclaim 13, wherein the diagnostic device includes a microphone and audiointerface coupling the microphone to the electronic processor forreceiving a cough sound of the patient.
 15. A diagnostic deviceaccording to claim 14, wherein the diagnostic application includes acough feature extraction engine and whereby in use the processor appliesthe cough sound to the cough feature extraction engine to produce coughfeatures thereof.
 16. A diagnostic device according to claim 15, whereinthe diagnostic device is configured by the diagnostic application in useto apply the cough features to the at least one of said diagnosticmodels.
 17. A method of automatically diagnosing pneumonia in a patientcomprising: using an input/output interface device to obtain values oftwo or more diagnostic parameters of the patient from a carer for thepatient; said diagnostic parameters comprising: breathing rate,existence of fever, existence of runny nose, number of days with runnynose, number of days with cough, existence of chest indrawing,temperature, BMI (body mass index), and oxygen saturation level; using aprocessor device operatively coupled to the input/output interface toapply the two or more diagnostic parameters to an electronic memorystoring a plurality of precompiled pneumonia diagnostic models tothereby identify an optimal diagnostic model of said plurality; applyingthe values of the two or more diagnostic parameters to the identifiedoptimal diagnostic model to generate a diagnosis output from thediagnostic model; and operating the input/output interface device inaccordance with the diagnosis output to indicate presence or absence ofpneumonia in the patient to the carer, for use by the carer in providingcare to the patient; wherein the pneumonia diagnostic models are derivedfrom investigation of a population of pneumonia positive patients andnon-pneumonia positive subjects.